### 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.asynchronous:
                self.log.info("Waiting for job %s to complete", job_id)
                self.hook.wait_for_job(job_id=job_id, region=self.region, project_id=self.project_id)
                self.log.info("Job %s completed successfully.", job_id)
            return job_id
        else:
            raise AirflowException("Create a job template before")
    def execute_complete(self, context, event=None) -> None:
        """
        Callback for when the trigger fires - returns immediately.
        Relies on trigger to throw an exception, otherwise it assumes execution was
        successful.
        """
        job_state = event["job_state"]
        job_id = event["job_id"]
        if job_state == JobStatus.State.ERROR:
            raise AirflowException(f"Job failed:\n{job_id}")
        if job_state == JobStatus.State.CANCELLED:
            raise AirflowException(f"Job was cancelled:\n{job_id}")
        self.log.info("%s completed successfully.", self.task_id)
        return job_id
    def on_kill(self) -> None:
        """
        Callback called when the operator is killed.
        Cancel any running job.
        """
        if self.dataproc_job_id:
            self.hook.cancel_job(project_id=self.project_id, job_id=self.dataproc_job_id, region=self.region)
class DataprocSubmitPigJobOperator(DataprocJobBaseOperator):