### 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)

self.log.info("Creating cluster: %s", self.cluster_name)
        hook = DataprocHook(gcp_conn_id=self.gcp_conn_id, impersonation_chain=self.impersonation_chain)
        
        DataprocLink.persist(
            context=context, task_instance=self, url=DATAPROC_CLUSTER_LINK, resource=self.cluster_name
        )
        try:
            
            operation = self._create_cluster(hook)
            if not self.deferrable:
                cluster = hook.wait_for_operation(
                    timeout=self.timeout, result_retry=self.retry, operation=operation
                )
                self.log.info("Cluster created.")
                return Cluster.to_dict(cluster)
            else:
                self.defer(
                    trigger=DataprocClusterTrigger(
                        cluster_name=self.cluster_name,
                        project_id=self.project_id,
                        region=self.region,
                        gcp_conn_id=self.gcp_conn_id,
                        impersonation_chain=self.impersonation_chain,
                        polling_interval_seconds=self.polling_interval_seconds,
                    ),
                    method_name="execute_complete",
                )
        except AlreadyExists: