### 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)

"""
    Upsert BigQuery table
    .. seealso::
        For more information on how to use this operator, take a look at the guide:
        :ref:`howto/operator:BigQueryUpsertTableOperator`
    :param dataset_id: A dotted
        ``(<project>.|<project>:)<dataset>`` that indicates which dataset
        will be updated. (templated)
    :param table_resource: a table resource. see
        https://cloud.google.com/bigquery/docs/reference/v2/tables#resource
    :param project_id: The name of the project where we want to update the dataset.
        Don't need to provide, if projectId in dataset_reference.
    :param gcp_conn_id: (Optional) The connection ID used to connect to Google Cloud.
    :param delegate_to: The account to impersonate, if any.
        For this to work, the service account making the request must have domain-wide
        delegation enabled.
    :param location: The location used for the operation.
    :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).
    """
    template_fields: Sequence[str] = (
        "dataset_id",
        "table_resource",
        "impersonation_chain",