### 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)

bq_hook = BigQueryHook(
            gcp_conn_id=self.gcp_conn_id,
            location=self.location,
            impersonation_chain=self.impersonation_chain,
        )
        if self.table_resource:
            table = bq_hook.create_empty_table(
                table_resource=self.table_resource,
            )
            BigQueryTableLink.persist(
                context=context,
                task_instance=self,
                dataset_id=table.to_api_repr()["tableReference"]["datasetId"],
                project_id=table.to_api_repr()["tableReference"]["projectId"],
                table_id=table.to_api_repr()["tableReference"]["tableId"],
            )
            return
        if not self.schema_fields and self.schema_object and self.source_format != "DATASTORE_BACKUP":
            gcs_hook = GCSHook(
                gcp_conn_id=self.google_cloud_storage_conn_id,
                impersonation_chain=self.impersonation_chain,
            )
            schema_fields = json.loads(
                gcs_hook.download(self.gcs_schema_bucket, self.schema_object).decode("utf-8")
            )
        else:
            schema_fields = self.schema_fields
        source_uris = [f"gs://{self.bucket}/{source_object}" for source_object in self.source_objects]
        project_id, dataset_id, table_id = bq_hook.split_tablename(
            table_input=self.destination_project_dataset_table,