### Description

There might be an use case where the export_format can be based on some dynamic values. So, adding export_format will help developers in future

### Use case/motivation

_No response_

### Related issues

_No response_

### Are you willing to submit a PR?

- [X] 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 job.error_result:
            raise AirflowException(f"BigQuery job {job.job_id} failed: {job.error_result}")
    def _prepare_configuration(self):
        source_project, source_dataset, source_table = self.hook.split_tablename(
            table_input=self.source_project_dataset_table,
            default_project_id=self.project_id or self.hook.project_id,
            var_name="source_project_dataset_table",
        )
        configuration: dict[str, Any] = {
            "extract": {
                "sourceTable": {
                    "projectId": source_project,
                    "datasetId": source_dataset,
                    "tableId": source_table,
                },
                "compression": self.compression,
                "destinationUris": self.destination_cloud_storage_uris,
                "destinationFormat": self.export_format,
            }
        }
        if self.labels:
            configuration["labels"] = self.labels
        if self.export_format == "CSV":
            
            
            
            configuration["extract"]["fieldDelimiter"] = self.field_delimiter
            configuration["extract"]["printHeader"] = self.print_header
        return configuration
    def _submit_job(