### Apache Airflow version

Other Airflow 2 version (please specify below)

### What happened

Airflow version: 2.5.3
Description: While using BigQueryCreateExternalTableOperator, when we to create external table on top of parquet files located in GCS, airflow shows below error:
google.api_core.exceptions.BadRequest: 400 POST ... failed. CsvOptions can only be specified if storage format is CSV.

No csvOptions are mentioned while passing the source_format as Parquet. There seems to be an issue with bigquery.py hook.

### What you think should happen instead

_No response_

### How to reproduce

While using BigQueryCreateExternalTableOperator, try to create external table on top of parquet files located in GCS. Sample usage:
`create_external_table = BigQueryCreateExternalTableOperator(
    task_id="create_external_table",
    destination_project_dataset_table="table_name",
    bucket=GCS_BUCKET_NAME,
    source_objects=[<list of URIs of parquet files>],
    source_format="PARQUET",
    autodetect=True,
    gcp_conn_id="gcp_conn_id",
    google_cloud_storage_conn_id="test_conn_id",
)`

### Operating System

-

### Versions of Apache Airflow Providers

_No response_

### Deployment

Official Apache Airflow Helm Chart

### Deployment details

_No response_

### Anything else

_No response_

### Are you willing to submit PR?

- [ ] 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)

if self.as_dict:
                table_data = [{k: v for k, v in row.items()} for row in rows]
            else:
                table_data = [row.values() for row in rows]
            return table_data
        job = self._submit_job(hook, job_id="")
        context["ti"].xcom_push(key="job_id", value=job.job_id)
        self.defer(
            timeout=self.execution_timeout,
            trigger=BigQueryGetDataTrigger(
                conn_id=self.gcp_conn_id,
                job_id=job.job_id,
                dataset_id=self.dataset_id,
                table_id=self.table_id,
                project_id=self.job_project_id or hook.project_id,
                poll_interval=self.poll_interval,
                as_dict=self.as_dict,
            ),
            method_name="execute_complete",
        )
    def execute_complete(self, context: Context, event: dict[str, Any]) -> Any:
        """Callback for when the trigger fires.
        This returns immediately. It relies on trigger to throw an exception,
        otherwise it assumes execution was successful.
        """
        if event["status"] == "error":
            raise AirflowException(event["message"])
        self.log.info("Total extracted rows: %s", len(event["records"]))
        return event["records"]
class BigQueryExecuteQueryOperator(GoogleCloudBaseOperator):