### 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)

if not self.use_if_exists:
                raise
            self.log.info("Cluster already exists.")
            cluster = self._get_cluster(hook)
        
        self._handle_error_state(hook, cluster)
        if cluster.status.state == cluster.status.State.CREATING:
            
            cluster = self._wait_for_cluster_in_creating_state(hook)
            self._handle_error_state(hook, cluster)
        elif cluster.status.state == cluster.status.State.DELETING:
            
            self._wait_for_cluster_in_deleting_state(hook)
            
            cluster = self._create_cluster(hook)
            self._handle_error_state(hook, cluster)
        return Cluster.to_dict(cluster)
    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)
        return event["cluster"]
class DataprocScaleClusterOperator(GoogleCloudBaseOperator):