### Apache Airflow version

Other Airflow 2 version (please specify below)
Airflow 2.4.2

### What happened

BaseSQLToGCSOperator creates row group for each rows during parquet generation, which cause compression not work and increase file size.
![image](https://github.com/apache/airflow/assets/51909776/bf256065-c130-4354-81c7-8ca2ed4e8d93)


### What you think should happen instead

_No response_

### How to reproduce

OracleToGCSOperator(
        task_id='oracle_to_gcs_parquet_test',
        gcp_conn_id=GCP_CONNECTION,
        oracle_conn_id=ORACLE_CONNECTION,
        sql='',
        bucket=GCS_BUCKET_NAME,
        filename='',
        export_format='parquet',
)

### Operating System

CentOS Linux 7

### Versions of Apache Airflow Providers

apache-airflow-providers-apache-hive  2.1.0
apache-airflow-providers-apache-sqoop 2.0.2
apache-airflow-providers-celery       3.0.0
apache-airflow-providers-common-sql   1.2.0
apache-airflow-providers-ftp          3.1.0
apache-airflow-providers-google       8.4.0
apache-airflow-providers-http         4.0.0
apache-airflow-providers-imap         3.0.0
apache-airflow-providers-mysql        3.0.0
apache-airflow-providers-oracle       2.1.0
apache-airflow-providers-salesforce   5.3.0
apache-airflow-providers-sqlite       3.2.1

### 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)

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",
            exclude_columns=EXCLUDE_COLUMNS,
        )
        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_WITH_EXCLUDE_COLUMNS)
    def test__write_local_data_files_parquet_with_partition_columns(self):