### 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)

polling_interval_seconds: int = 10,
        **kwargs,
    ) -> None:
        super().__init__(**kwargs)
        if deferrable and polling_interval_seconds <= 0:
            raise ValueError("Invalid value for polling_interval_seconds. Expected value greater than 0")
        self.template = template
        self.project_id = project_id
        self.region = region
        self.template = template
        self.request_id = request_id
        self.retry = retry
        self.timeout = timeout
        self.metadata = metadata
        self.gcp_conn_id = gcp_conn_id
        self.impersonation_chain = impersonation_chain
        self.deferrable = deferrable
        self.polling_interval_seconds = polling_interval_seconds
    def execute(self, context: Context):
        self.log.info("Instantiating Inline Template")
        hook = DataprocHook(gcp_conn_id=self.gcp_conn_id, impersonation_chain=self.impersonation_chain)
        operation = hook.instantiate_inline_workflow_template(
            template=self.template,
            project_id=self.project_id or hook.project_id,
            region=self.region,
            request_id=self.request_id,
            retry=self.retry,
            timeout=self.timeout,
            metadata=self.metadata,
        )