### Apache Airflow version

Other Airflow 2 version (please specify below) : 2.6.1

### What happened

I encountered an issue while running an AutoML task. The task failed with an authentication error due to the inability to find the project ID. Here are the details of the error:

```
[2023-06-17T18:42:48.916+0530] {[taskinstance.py:1308](http://taskinstance.py:1308/)} INFO - Starting attempt 1 of 1
[2023-06-17T18:42:48.931+0530] {[taskinstance.py:1327](http://taskinstance.py:1327/)} INFO - Executing <Task(CreateAutoMLTabularTrainingJobOperator): auto_ml_tabular_task> on 2023-06-17 13:12:33+00:00
[2023-06-17T18:42:48.964+0530] {[standard_task_runner.py:57](http://standard_task_runner.py:57/)} INFO - Started process 12974 to run task
[2023-06-17T18:42:48.971+0530] {[standard_task_runner.py:84](http://standard_task_runner.py:84/)} INFO - Running: ['airflow', 'tasks', 'run', 'vi_create_auto_ml_tabular_training_job_dag', 'auto_ml_tabular_task', 'manual__2023-06-17T13:12:33+00:00', '--job-id', '175', '--raw', '--subdir', 'DAGS_FOLDER/vi_create_model_train.py', '--cfg-path', '/tmp/tmprijpfzql']
[2023-06-17T18:42:48.974+0530] {[standard_task_runner.py:85](http://standard_task_runner.py:85/)} INFO - Job 175: Subtask auto_ml_tabular_task
[2023-06-17T18:42:49.043+0530] {[logging_mixin.py:149](http://logging_mixin.py:149/)} INFO - Changing /mnt/d/projects/airflow/logs/dag_id=vi_create_auto_ml_tabular_training_job_dag/run_id=manual__2023-06-17T13:12:33+00:00/task_id=auto_ml_tabular_task permission to 509
[2023-06-17T18:42:49.044+0530] {[task_command.py:410](http://task_command.py:410/)} INFO - Running <TaskInstance: vi_create_auto_ml_tabular_training_job_dag.auto_ml_tabular_task manual__2023-06-17T13:12:33+00:00 [running]> on host DESKTOP-EIFUHU2.localdomain
[2023-06-17T18:42:49.115+0530] {[taskinstance.py:1545](http://taskinstance.py:1545/)} INFO - Exporting env vars: AIRFLOW_CTX_DAG_OWNER='airflow' AIRFLOW_CTX_DAG_ID='vi_create_auto_ml_tabular_training_job_dag' AIRFLOW_CTX_TASK_ID='auto_ml_tabular_task' AIRFLOW_CTX_EXECUTION_DATE='2023-06-17T13:12:33+00:00' AIRFLOW_CTX_TRY_NUMBER='1' AIRFLOW_CTX_DAG_RUN_ID='manual__2023-06-17T13:12:33+00:00'
[2023-06-17T18:42:49.120+0530] {[base.py:73](http://base.py:73/)} INFO - Using connection ID 'gcp_conn' for task execution.
[2023-06-17T18:42:52.123+0530] {[_metadata.py:141](http://_metadata.py:141/)} WARNING - Compute Engine Metadata server unavailable on attempt 1 of 3. Reason: timed out
[2023-06-17T18:42:55.125+0530] {[_metadata.py:141](http://_metadata.py:141/)} WARNING - Compute Engine Metadata server unavailable on attempt 2 of 3. Reason: timed out
[2023-06-17T18:42:58.128+0530] {[_metadata.py:141](http://_metadata.py:141/)} WARNING - Compute Engine Metadata server unavailable on attempt 3 of 3. Reason: timed out
[2023-06-17T18:42:58.129+0530] {[_default.py:340](http://_default.py:340/)} WARNING - Authentication failed using Compute Engine authentication due to unavailable metadata server.
[2023-06-17T18:42:58.131+0530] {[taskinstance.py:1824](http://taskinstance.py:1824/)} ERROR - Task failed with exception
Traceback (most recent call last):
  File "/mnt/d/projects/tvenv/lib/python3.8/site-packages/google/cloud/aiplatform/initializer.py", line 244, in project
    self._set_project_as_env_var_or_google_auth_default()
  File "/mnt/d/projects/tvenv/lib/python3.8/site-packages/google/cloud/aiplatform/initializer.py", line 81, in _set_project_as_env_var_or_google_auth_default
    credentials, project = google.auth.default()
  File "/mnt/d/projects/tvenv/lib/python3.8/site-packages/google/auth/_default.py", line 692, in default
    raise exceptions.DefaultCredentialsError(_CLOUD_SDK_MISSING_CREDENTIALS)
google.auth.exceptions.DefaultCredentialsError: Your default credentials were not found. To set up Application Default Credentials, see [https://cloud.google.com/docs/authentication/external/set-up-adc](https://cloud.google.com/docs/authentication/external/set-up-adc?authuser=0) for more information.

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/mnt/d/projects/tvenv/lib/python3.8/site-packages/airflow/providers/google/cloud/operators/vertex_ai/auto_ml.py", line 359, in execute
    dataset=datasets.TabularDataset(dataset_name=self.dataset_id),
  File "/mnt/d/projects/tvenv/lib/python3.8/site-packages/google/cloud/aiplatform/datasets/dataset.py", line 77, in __init__
    super().__init__(
  File "/mnt/d/projects/tvenv/lib/python3.8/site-packages/google/cloud/aiplatform/base.py", line 925, in __init__
    VertexAiResourceNoun.__init__(
  File "/mnt/d/projects/tvenv/lib/python3.8/site-packages/google/cloud/aiplatform/base.py", line 507, in __init__
    self.project = project or initializer.global_config.project
  File "/mnt/d/projects/tvenv/lib/python3.8/site-packages/google/cloud/aiplatform/initializer.py", line 247, in project
    raise GoogleAuthError(project_not_found_exception_str) from exc
google.auth.exceptions.GoogleAuthError: Unable to find your project. Please provide a project ID by:
- Passing a constructor argument
- Using aiplatform.init()
- Setting project using 'gcloud config set project my-project'
- Setting a GCP environment variable
[2023-06-17T18:42:58.139+0530] {[taskinstance.py:1345](http://taskinstance.py:1345/)} INFO - Marking task as FAILED. dag_id=vi_create_auto_ml_tabular_training_job_dag, task_id=auto_ml_tabular_task, execution_date=20230617T131233, start_date=20230617T131248, end_date=20230617T131258
[2023-06-17T18:42:58.152+0530] {[standard_task_runner.py:104](http://standard_task_runner.py:104/)} ERROR - Failed to execute job 175 for task auto_ml_tabular_task (Unable to find your project. Please provide a project ID by:
- Passing a constructor argument
- Using aiplatform.init()
- Setting project using 'gcloud config set project my-project'
- Setting a GCP environment variable; 12974)
[2023-06-17T18:42:58.166+0530] {[local_task_job_runner.py:225](http://local_task_job_runner.py:225/)} INFO - Task exited with return code 1
[2023-06-17T18:42:58.183+0530] {[taskinstance.py:2651](http://taskinstance.py:2651/)} INFO - 0 downstream tasks scheduled from follow-on schedule check
```

### What you think should happen instead

Expected Behavior:
The AutoML task should execute successfully, using the appropriate project ID and credentials for authentication given as per the gcs_con_id provided in the dag.

Actual Behavior:
The task fails with an authentication error due to the inability to find the project ID and default credentials.

### How to reproduce

To reproduce the issue and execute the CreateAutoMLTabularTrainingJobOperator task in Apache Airflow, follow these steps:

Ensure that Apache Airflow is installed. If not, run the following command to install it:
```
pip install apache-airflow
```
Create an instance of the CreateAutoMLTabularTrainingJobOperator within the DAG context:
```
    create_auto_ml_tabular_training_job = CreateAutoMLTabularTrainingJobOperator(
        gcp_conn_id='gcp_conn',
        task_id="auto_ml_tabular_task",
        display_name=TABULAR_DISPLAY_NAME,
        optimization_prediction_type="regression",
        optimization_objective="minimize-rmse",
        #column_transformations=COLUMN_TRANSFORMATIONS,
        dataset_id=tabular_dataset_id, # Get this // 
        target_column="mean_temp",
        training_fraction_split=0.8,
        validation_fraction_split=0.1,
        test_fraction_split=0.1,
        model_display_name='your-model-display-name',
        disable_early_stopping=False,
        region=REGION,
        project_id=PROJECT_ID
    )
```

Start the Apache Airflow scheduler and webserver. Open a terminal or command prompt and run the following commands:

```
# Start the scheduler
airflow scheduler

# Start the webserver
airflow webserver
```

Access the Apache Airflow web UI by opening a web browser and navigating to http://localhost:8080. Ensure that the scheduler and webserver are running without any errors.

Navigate to the DAGs page in the Airflow UI and locate the vi_create_auto_ml_tabular_training_job_dag DAG. Trigger the DAG manually, either by clicking the "Trigger DAG" button or using the Airflow CLI command.

Monitor the DAG execution status and check if the auto_ml_tabular_task completes successfully or encounters any errors.

### Operating System

DISTRIB_ID=Ubuntu DISTRIB_RELEASE=20.04 DISTRIB_CODENAME=focal DISTRIB_DESCRIPTION="Ubuntu 20.04 LTS"

### Versions of Apache Airflow Providers

$ pip freeze
aiofiles==23.1.0
aiohttp==3.8.4
aiosignal==1.3.1
alembic==1.11.1
anyio==3.7.0
apache-airflow==2.6.1
apache-airflow-providers-common-sql==1.5.1
apache-airflow-providers-ftp==3.4.1
apache-airflow-providers-google==10.1.1
apache-airflow-providers-http==4.4.1
apache-airflow-providers-imap==3.2.1
apache-airflow-providers-sqlite==3.4.1
apispec==5.2.2
argcomplete==3.1.1
asgiref==3.7.2
async-timeout==4.0.2
attrs==23.1.0
Babel==2.12.1
backoff==2.2.1
blinker==1.6.2
cachelib==0.9.0
cachetools==5.3.1
cattrs==23.1.2
certifi==2023.5.7
cffi==1.15.1
chardet==5.1.0
charset-normalizer==3.1.0
click==8.1.3
clickclick==20.10.2
colorama==0.4.6
colorlog==4.8.0
ConfigUpdater==3.1.1
connexion==2.14.2
cron-descriptor==1.4.0
croniter==1.4.1
cryptography==41.0.1
db-dtypes==1.1.1
Deprecated==1.2.14
dill==0.3.6
dnspython==2.3.0
docutils==0.20.1
email-validator==1.3.1
exceptiongroup==1.1.1
Flask==2.2.5
Flask-AppBuilder==4.3.0
Flask-Babel==2.0.0
Flask-Caching==2.0.2
Flask-JWT-Extended==4.5.2
Flask-Limiter==3.3.1
Flask-Login==0.6.2
flask-session==0.5.0
Flask-SQLAlchemy==2.5.1
Flask-WTF==1.1.1
frozenlist==1.3.3
future==0.18.3
gcloud-aio-auth==4.2.1
gcloud-aio-bigquery==6.3.0
gcloud-aio-storage==8.2.0
google-ads==21.2.0
google-api-core==2.11.1
google-api-python-client==2.89.0
google-auth==2.20.0
google-auth-httplib2==0.1.0
google-auth-oauthlib==1.0.0
google-cloud-aiplatform==1.26.0
google-cloud-appengine-logging==1.3.0
google-cloud-audit-log==0.2.5
google-cloud-automl==2.11.1
google-cloud-bigquery==3.11.1
google-cloud-bigquery-datatransfer==3.11.1
google-cloud-bigquery-storage==2.20.0
google-cloud-bigtable==2.19.0
google-cloud-build==3.16.0
google-cloud-compute==1.11.0
google-cloud-container==2.24.0
google-cloud-core==2.3.2
google-cloud-datacatalog==3.13.0
google-cloud-dataflow-client==0.8.3
google-cloud-dataform==0.5.1
google-cloud-dataplex==1.5.0
google-cloud-dataproc==5.4.1
google-cloud-dataproc-metastore==1.11.0
google-cloud-dlp==3.12.1
google-cloud-kms==2.17.0
google-cloud-language==2.10.0
google-cloud-logging==3.5.0
google-cloud-memcache==1.7.1
google-cloud-monitoring==2.15.0
google-cloud-orchestration-airflow==1.9.0
google-cloud-os-login==2.9.1
google-cloud-pubsub==2.17.1
google-cloud-redis==2.13.0
google-cloud-resource-manager==1.10.1
google-cloud-secret-manager==2.16.1
google-cloud-spanner==3.36.0
google-cloud-speech==2.20.0
google-cloud-storage==2.9.0
google-cloud-tasks==2.13.1
google-cloud-texttospeech==2.14.1
google-cloud-translate==3.11.1
google-cloud-videointelligence==2.11.2
google-cloud-vision==3.4.2
google-cloud-workflows==1.10.1
google-crc32c==1.5.0
google-resumable-media==2.5.0
googleapis-common-protos==1.59.1
graphviz==0.20.1
greenlet==2.0.2
grpc-google-iam-v1==0.12.6
grpcio==1.54.2
grpcio-gcp==0.2.2
grpcio-status==1.54.2
gunicorn==20.1.0
h11==0.14.0
httpcore==0.17.2
httplib2==0.22.0
httpx==0.24.1
idna==3.4
importlib-metadata==4.13.0
importlib-resources==5.12.0
inflection==0.5.1
itsdangerous==2.1.2
Jinja2==3.1.2
json-merge-patch==0.2
jsonschema==4.17.3
lazy-object-proxy==1.9.0
limits==3.5.0
linkify-it-py==2.0.2
lockfile==0.12.2
looker-sdk==23.10.0
Mako==1.2.4
Markdown==3.4.3
markdown-it-py==3.0.0
MarkupSafe==2.1.3
marshmallow==3.19.0
marshmallow-enum==1.5.1
marshmallow-oneofschema==3.0.1
marshmallow-sqlalchemy==0.26.1
mdit-py-plugins==0.4.0
mdurl==0.1.2
multidict==6.0.4
numpy==1.24.3
oauthlib==3.2.2
ordered-set==4.1.0
packaging==23.1
pandas==2.0.2
pandas-gbq==0.19.2
pathspec==0.9.0
pendulum==2.1.2
pkgutil-resolve-name==1.3.10
pluggy==1.0.0
prison==0.2.1
proto-plus==1.22.2
protobuf==4.23.3
psutil==5.9.5
pyarrow==12.0.1
pyasn1==0.4.8
pyasn1-modules==0.2.8
pycparser==2.21
pydantic==1.10.9
pydata-google-auth==1.8.0
Pygments==2.15.1
PyJWT==2.7.0
pyOpenSSL==23.2.0
pyparsing==3.0.9
pyrsistent==0.19.3
python-daemon==3.0.1
python-dateutil==2.8.2
python-nvd3==0.15.0
python-slugify==8.0.1
pytz==2023.3
pytzdata==2020.1
PyYAML==6.0
requests==2.31.0
requests-oauthlib==1.3.1
requests-toolbelt==1.0.0
rfc3339-validator==0.1.4
rich==13.4.2
rich-argparse==1.1.1
rsa==4.9
setproctitle==1.3.2
Shapely==1.8.5.post1
six==1.16.0
sniffio==1.3.0
SQLAlchemy==1.4.48
sqlalchemy-bigquery==1.6.1
SQLAlchemy-JSONField==1.0.1.post0
SQLAlchemy-Utils==0.41.1
sqlparse==0.4.4
tabulate==0.9.0
tenacity==8.2.2
termcolor==2.3.0
text-unidecode==1.3
typing-extensions==4.6.3
tzdata==2023.3
uc-micro-py==1.0.2
unicodecsv==0.14.1
uritemplate==4.1.1
urllib3==2.0.3
Werkzeug==2.3.6
wrapt==1.15.0
WTForms==3.0.1
yarl==1.9.2
zipp==3.15.0


### Deployment

Virtualenv installation

### Deployment details

$ airflow info

Apache Airflow
version                | 2.6.1
executor               | SequentialExecutor
task_logging_handler   | airflow.utils.log.file_task_handler.FileTaskHandler
sql_alchemy_conn       | sqlite:////home/test1/airflow/airflow.db
dags_folder            | /mnt/d/projects/airflow/dags
plugins_folder         | /home/test1/airflow/plugins                        
base_log_folder        | /mnt/d/projects/airflow/logs
remote_base_log_folder |


System info
OS              | Linux
architecture    | x86_64
uname           | uname_result(system='Linux', node='DESKTOP-EIFUHU2', release='4.4.0-19041-Microsoft', version='#1237-Microsoft Sat Sep 11 14:32:00 PST      
                | 2021', machine='x86_64', processor='x86_64')
locale          | ('en_US', 'UTF-8')
python_version  | 3.8.10 (default, May 26 2023, 14:05:08)  [GCC 9.4.0]
python_location | /mnt/d/projects/tvenv/bin/python3


Tools info
git             | git version 2.25.1
ssh             | OpenSSH_8.2p1 Ubuntu-4, OpenSSL 1.1.1f  31 Mar 2020
kubectl         | NOT AVAILABLE
gcloud          | NOT AVAILABLE
cloud_sql_proxy | NOT AVAILABLE
mysql           | NOT AVAILABLE                                      
sqlite3         | NOT AVAILABLE
psql            | NOT AVAILABLE


Paths info
airflow_home    | /home/test1/airflow
system_path     | /mnt/d/projects/tvenv/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/mnt/c/Program Files     
                | (x86)/Microsoft SDKs/Azure/CLI2/wbin:/mnt/c/Program Files/Python39/Scripts/:/mnt/c/Program
                | Files/Python39/:/mnt/c/Windows/system32:/mnt/c/Windows:/mnt/c/Windows/System32/Wbem:/mnt/c/Windows/System32/WindowsPowerShell/v1.0/:/mnt/c/W
                | indows/System32/OpenSSH/:/mnt/c/Users/ibrez/AppData/Roaming/nvm:/mnt/c/Program Files/nodejs:/mnt/c/Program
                | Files/dotnet/:/mnt/c/Windows/system32/config/systemprofile/AppData/Local/Microsoft/WindowsApps:/mnt/c/Users/test1/AppData/Local/Microsoft/Wi
                | ndowsApps:/snap/bin
python_path     | /mnt/d/projects/tvenv/bin:/usr/lib/python38.zip:/usr/lib/python3.8:/usr/lib/python3.8/lib-dynload:/mnt/d/projects/tvenv/lib/python3.8/site-p
                | ackages:/mnt/d/projects/airflow/dags:/home/test1/airflow/config:/home/test1/airflow/plugins
airflow_on_path | True


Providers info
apache-airflow-providers-common-sql | 1.5.1 
apache-airflow-providers-ftp        | 3.4.1
apache-airflow-providers-google     | 10.1.1
apache-airflow-providers-http       | 4.4.1
apache-airflow-providers-imap       | 3.2.1
apache-airflow-providers-sqlite     | 3.4.1



### Anything else

To me it seems like the issue is at the line 
```
 dataset=datasets.TabularDataset(dataset_name=self.dataset_id),
```

[here](https://github.com/apache/airflow/blob/main/airflow/providers/google/cloud/operators/vertex_ai/auto_ml.py)

The details like project id and credentials are not being passed to the TabularDataset class which causes issues down the like for 
```
GoogleAuthError(project_not_found_exception_str)
```

### Are you willing to submit PR?

- [X] Yes I am willing to submit a PR!

### Code of Conduct

- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

@mock.patch("google.cloud.aiplatform.datasets.TabularDataset")
    @mock.patch(VERTEX_AI_PATH.format("auto_ml.AutoMLHook"))
    def test_execute(self, mock_hook, mock_dataset):
        mock_hook.return_value.create_auto_ml_tabular_training_job.return_value = (None, "training_id")
        op = CreateAutoMLTabularTrainingJobOperator(
            task_id=TASK_ID,
            gcp_conn_id=GCP_CONN_ID,
            impersonation_chain=IMPERSONATION_CHAIN,
            display_name=DISPLAY_NAME,
            dataset_id=TEST_DATASET_ID,
            target_column=None,
            optimization_prediction_type=None,
            sync=True,
            region=GCP_LOCATION,
            project_id=GCP_PROJECT,
        )
        op.execute(context={"ti": mock.MagicMock()})
        mock_hook.assert_called_once_with(gcp_conn_id=GCP_CONN_ID, impersonation_chain=IMPERSONATION_CHAIN)
        mock_dataset.assert_called_once_with(dataset_name=TEST_DATASET_ID)
        mock_hook.return_value.create_auto_ml_tabular_training_job.assert_called_once_with(