### 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.workflow_id = operation.operation.name.split("/")[-1]
        DataprocLink.persist(
            context=context, task_instance=self, url=DATAPROC_WORKFLOW_LINK, resource=self.workflow_id
        )
        self.log.info("Template instantiated. Workflow Id : %s", self.workflow_id)
        if not self.deferrable:
            hook.wait_for_operation(timeout=self.timeout, result_retry=self.retry, operation=operation)
            self.log.info("Workflow %s completed successfully", self.workflow_id)
        else:
            self.defer(
                trigger=DataprocWorkflowTrigger(
                    name=operation.operation.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",
            )
    def execute_complete(self, context, event=None) -> None:
        """Callback for when the trigger fires.
        This returns immediately. It relies on trigger to throw an exception,
        otherwise it assumes execution was successful.
        """
        if event["status"] == "failed" or event["status"] == "error":
            self.log.exception("Unexpected error in the operation.")
            raise AirflowException(event["message"])
        self.log.info("Workflow %s completed successfully", event["operation_name"])
class DataprocInstantiateInlineWorkflowTemplateOperator(GoogleCloudBaseOperator):