### Apache Airflow Provider(s)

google

### Versions of Apache Airflow Providers

apache-airflow-providers-google==8.6.0

### Apache Airflow version

2.5.0

### Operating System

Debian GNU/Linux

### Deployment

Astronomer

### Deployment details

_No response_

### What happened

PR `Expose SQL to GCS Metadata (https://github.com/apache/airflow/pull/24382)` made a breaking change [here](https://github.com/apache/airflow/blob/3eee33ac8cb74cfbb08bce9090e9c601cf98da44/airflow/providers/google/cloud/transfers/sql_to_gcs.py#L286) that results in no files being returned when there are no data rows (empty table) rather than a single empty file as in the past.

### What you think should happen instead

I would like to preserve the original behavior of having at least one file returned even if it is empty. Or to make that behavior optional via a new parameter.

The original behavior can be implemented with the following code change:
FROM:
```
if file_to_upload["file_row_count"] > 0:
            yield file_to_upload
```
TO:
```
if file_no == 0 or file_to_upload["file_row_count"] > 0:
            yield file_to_upload
```

### How to reproduce

Create a DAG that uses BaseSQLToGCSOperator with a SQL command that references an empty SQL table or returns no rows. The `execute` method will not write any files.

### 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)

op = DummySQLToGCSOperator(
            sql=SQL,
            bucket=BUCKET,
            filename=FILENAME,
            task_id=TASK_ID,
            schema_filename=SCHEMA_FILE,
            export_format="json",
            gzip=False,
            schema=SCHEMA,
            gcp_conn_id="google_cloud_default",
        )
        cursor = MagicMock()
        cursor.__iter__.return_value = INPUT_DATA
        cursor.description = CURSOR_DESCRIPTION
        files = op._write_local_data_files(cursor)
        file = next(files)["file_handle"]
        file.flush()
        df = pd.read_json(file.name, orient="records", lines=True)
        assert df.equals(OUTPUT_DF)
    def test__write_local_data_files_parquet(self):