### Apache Airflow Provider(s)

google

### Versions of Apache Airflow Providers

I'm using `apache-airflow-providers-google==8.2.0`, but it looks like the relevant code that's causing this to occur is still in use as of `8.8.0`.

### Apache Airflow version

2.3.2

### Operating System

Debian (from Docker image `apache/airflow:2.3.2-python3.10`)

### Deployment

Official Apache Airflow Helm Chart

### Deployment details

Deployed on an EKS cluster via Helm.

### What happened

The first task in one of my DAGs is to create an empty BigQuery table using the `BigQueryCreateEmptyTableOperator` as follows:

```python
create_staging_table = BigQueryCreateEmptyTableOperator(
    task_id="create_staging_table",
    dataset_id="my_dataset",
    table_id="tmp_table",
    schema_fields=[
        {"name": "field_1", "type": "TIMESTAMP", "mode": "NULLABLE"},
        {"name": "field_2", "type": "INTEGER", "mode": "NULLABLE"},
        {"name": "field_3", "type": "INTEGER", "mode": "NULLABLE"}
    ],
    exists_ok=False
)
```
Note that `exists_ok=False` explicitly here, but it is also the default value.

This task exits with a `SUCCESS` status even when `my_dataset.tmp_table` already exists in a given BigQuery project. The task returns the following logs:

```
[2023-02-02, 05:52:29 UTC] {bigquery.py:875} INFO - Creating table
[2023-02-02, 05:52:29 UTC] {bigquery.py:901} INFO - Table my_dataset.tmp_table already exists.
[2023-02-02, 05:52:30 UTC] {taskinstance.py:1395} INFO - Marking task as SUCCESS. dag_id=my_fake_dag, task_id=create_staging_table, execution_date=20230202T044000, start_date=20230202T055229, end_date=20230202T055230
[2023-02-02, 05:52:30 UTC] {local_task_job.py:156} INFO - Task exited with return code 0
```

### What you think should happen instead

Setting `exists_ok=False` should raise an exception and exit the task with a `FAILED` status if the table being created already exists in BigQuery.

### How to reproduce

1. Deploy Airflow 2.3.2 running Python 3.10 in some capacity
2. Ensure `apache-airflow-providers-google==8.2.0` (or 8.8.0, as I don't believe the issue has been fixed) is installed on the deployment.
3. Set up a GCP project and create a BigQuery dataset.
4. Create an empty BigQuery table with a schema.
5. Create a DAG that uses the `BigQueryCreateEmptyTableOperator` to create a new BigQuery table.
6. Run the DAG from Step 5 on the Airflow instance deployed in Step 1.
7.  Observe the task's status.

### Anything else

I believe the silent failure may be occurring [here](https://github.com/apache/airflow/blob/main/airflow/providers/google/cloud/operators/bigquery.py#L1377), as the `except` statement results in a log output, but doesn't actually raise an exception or change a state that would make the task fail.

If this is in fact the case, I'd be happy to submit a PR, but appreciate any input as to any error-handling standards/consistencies that this provider package maintains.

### 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)

against. Defaults to 7 days
    :param metrics_thresholds: a dictionary of ratios indexed by metrics, for
        example 'COUNT(*)': 1.5 would require a 50 percent or less difference
        between the current day, and the prior days_back.
    :param use_legacy_sql: Whether to use legacy SQL (true)
        or standard SQL (false).
    :param gcp_conn_id: (Optional) The connection ID used to connect to Google Cloud.
    :param location: The geographic location of the job. See details at:
        https://cloud.google.com/bigquery/docs/locations#specifying_your_location
    :param impersonation_chain: Optional service account to impersonate using short-term
        credentials, or chained list of accounts required to get the access_token
        of the last account in the list, which will be impersonated in the request.
        If set as a string, the account must grant the originating account
        the Service Account Token Creator IAM role.
        If set as a sequence, the identities from the list must grant
        Service Account Token Creator IAM role to the directly preceding identity, with first
        account from the list granting this role to the originating account (templated).
    :param labels: a dictionary containing labels for the table, passed to BigQuery
    :param deferrable: Run operator in the deferrable mode
    """
    template_fields: Sequence[str] = (
        "table",
        "gcp_conn_id",
        "sql1",
        "sql2",
        "impersonation_chain",
        "labels",
    )
    ui_color = BigQueryUIColors.CHECK.value
    def __init__(