### 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 self.batch_id:
                self.log.info("Attaching to the job (%s) if it is still running.", self.batch_id)
                result = hook.wait_for_batch(
                    batch_id=self.batch_id,
                    region=self.region,
                    project_id=self.project_id,
                    retry=self.retry,
                    timeout=self.timeout,
                    metadata=self.metadata,
                    wait_check_interval=self.polling_interval_seconds,
                )
        
        
        if result is None:
            raise AirflowException("The job could not be reattached because the id was generated.")
        
        
        
        batch_id = self.batch_id or result.name.split("/")[-1]
        link = DATAPROC_BATCH_LINK.format(region=self.region, project_id=self.project_id, resource=batch_id)
        if result.state == Batch.State.FAILED:
            raise AirflowException(f"Batch job {batch_id} failed.  Driver Logs: {link}")
        if result.state in (Batch.State.CANCELLED, Batch.State.CANCELLING):
            raise AirflowException(f"Batch job {batch_id} was cancelled. Driver logs: {link}")
        if result.state == Batch.State.STATE_UNSPECIFIED:
            raise AirflowException(f"Batch job {batch_id} unspecified. Driver logs: {link}")
        self.log.info("Batch job %s completed. Driver logs: %s", batch_id, link)
        DataprocLink.persist(context=context, task_instance=self, url=DATAPROC_BATCH_LINK, resource=batch_id)
        return Batch.to_dict(result)
    def execute_complete(self, context, event=None) -> None: