### Apache Airflow version

main (development)

### What happened

The DataprocCreateBatchOperator (Google provider) handles the case when a batch_id already exists in the Dataproc API by 'reattaching' to a potentially running job.  

Current reattachment logic uses the non-deferrable method even when the operator is in deferrable mode.  

### What you think should happen instead

The operator should reattach in deferrable mode. 

### How to reproduce

Create a DAG with a task of DataprocCreateBatchOperator that is long running.  Make DataprocCreateBatchOperator deferrable in the constructor.  

Restart local Airflow to simulate having to 'reattach' to a running job in Google Cloud Dataproc. 

The operator resumes using the running job but in the code path for the non-derferrable logic.  

### Operating System

macOS 13.4.1 (22F82)

### Versions of Apache Airflow Providers

Current main.

### Deployment

Official Apache Airflow Helm Chart

### Deployment details

_No response_

### Anything else

_No response_

### Are you willing to submit 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)

gcp_conn_id=self.gcp_conn_id,
                    impersonation_chain=self.impersonation_chain,
                    polling_interval_seconds=self.polling_interval_seconds,
                ),
                method_name="execute_complete",
            )
        self.log.info("Updated %s cluster.", self.cluster_name)
    def execute_complete(self, context: Context, event: dict[str, Any]) -> Any:
        """
        Callback for when the trigger fires - returns immediately.
        Relies on trigger to throw an exception, otherwise it assumes execution was
        successful.
        """
        cluster_state = event["cluster_state"]
        cluster_name = event["cluster_name"]
        if cluster_state == ClusterStatus.State.ERROR:
            raise AirflowException(f"Cluster is in ERROR state:\n{cluster_name}")
        self.log.info("%s completed successfully.", self.task_id)
class DataprocCreateBatchOperator(GoogleCloudBaseOperator):
    """Create a batch workload.
    :param project_id: Optional. The ID of the Google Cloud project that the cluster belongs to. (templated)
    :param region: Required. The Cloud Dataproc region in which to handle the request. (templated)
    :param batch: Required. The batch to create. (templated)
    :param batch_id: Optional. The ID to use for the batch, which will become the final component
        of the batch's resource name.
        This value must be 4-63 characters. Valid characters are /[a-z][0-9]-/. (templated)
    :param request_id: Optional. A unique id used to identify the request. If the server receives two
        ``CreateBatchRequest`` requests with the same id, then the second request will be ignored and
        the first ``google.longrunning.Operation`` created and stored in the backend is returned.
    :param retry: A retry object used to retry requests. If ``None`` is specified, requests will not be