### 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)

"""Configure a csv writer with the file_handle and write schema
        as headers for the new file.
        """
        csv_writer = csv.writer(file_handle, encoding="utf-8", delimiter=self.field_delimiter)
        csv_writer.writerow(schema)
        return csv_writer
    def _configure_parquet_file(self, file_handle, parquet_schema):
        parquet_writer = pq.ParquetWriter(file_handle.name, parquet_schema)
        return parquet_writer
    def _convert_parquet_schema(self, cursor):
        type_map = {
            "INTEGER": pa.int64(),
            "FLOAT": pa.float64(),
            "NUMERIC": pa.float64(),
            "BIGNUMERIC": pa.float64(),
            "BOOL": pa.bool_(),
            "STRING": pa.string(),
            "BYTES": pa.binary(),
            "DATE": pa.date32(),
            "DATETIME": pa.date64(),
            "TIMESTAMP": pa.timestamp("s"),
        }
        columns = [field[0] for field in cursor.description]
        bq_fields = [self.field_to_bigquery(field) for field in cursor.description]
        bq_types = [bq_field.get("type") if bq_field is not None else None for bq_field in bq_fields]
        pq_types = [type_map.get(bq_type, pa.string()) for bq_type in bq_types]
        parquet_schema = pa.schema(zip(columns, pq_types))
        return parquet_schema
    @abc.abstractmethod
    def query(self):