### Apache Airflow version

main (development)

### What happened

The provider operator for Google Cloud Dataproc Batches has two bugs: 

1. The running [operator](https://github.com/apache/airflow/blob/main/airflow/providers/google/cloud/operators/dataproc.py#L2123-L2124) returns successful even if the job transitions to State.CANCELLED or State.CANCELLING 
2. It [attempts](https://github.com/apache/airflow/blob/main/airflow/providers/google/cloud/operators/dataproc.py#L2154) to 'reattach' to a potentially running job if it AlreadyExists, but it sends the wrong type since 'result' is a Batch and needs Operation

### What you think should happen instead

A new hook that polls for batch job completion.  There is precedent for it in traditional dataproc with 'wait_for_job'.

### How to reproduce

Use the Breeze environment and a DAG that runs DataprocCreateBatchOperator.  Allow the first instance to start. 

Use the gcloud CLI to cancel the job. 

`gcloud dataproc batches cancel <batch_id> --project <project_id> --region <region>`

Observe that the task completes successfully after a 3-5 minute timeout, even though the job was cancelled. 

Run the task again with the same batch_id.  Observe the ValueError where it expects Operation but receives Batch



### Operating System

Darwin 5806 21.6.0 Darwin Kernel Version 21.6.0: Mon Aug 22 20:17:10 PDT 2022; root:xnu-8020.140.49~2/RELEASE_X86_64 x86_64

### Versions of Apache Airflow Providers

Same as dev (main) version. 

### Deployment

Other Docker-based deployment

### Deployment details

Observable in the Breeze environment, when running against real Google Infrastructure. 

### Anything else

Every time. 

### 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)

retry=RETRY,
            timeout=TIMEOUT,
            metadata=METADATA,
            impersonation_chain=IMPERSONATION_CHAIN,
        )
        op.execute(context=self.mock_context)
        mock_hook.assert_called_once_with(gcp_conn_id=GCP_CONN_ID, impersonation_chain=IMPERSONATION_CHAIN)
        mock_hook.return_value.create_cluster.assert_called_once_with(**create_cluster_args)
        
        self.extra_links_manager_mock.assert_has_calls(expected_calls, any_order=False)
        to_dict_mock.assert_called_once_with(mock_hook().wait_for_operation())
        self.mock_ti.xcom_push.assert_called_once_with(
            key="conf",
            value=DATAPROC_CLUSTER_CONF_EXPECTED,
            execution_date=None,
        )
    @mock.patch(DATAPROC_PATH.format("Cluster.to_dict"))
    @mock.patch(DATAPROC_PATH.format("DataprocHook"))
    def test_execute_if_cluster_exists(self, mock_hook, to_dict_mock):
        mock_hook.return_value.create_cluster.side_effect = [AlreadyExists("test")]
        mock_hook.return_value.get_cluster.return_value.status.state = 0
        op = DataprocCreateClusterOperator(
            task_id=TASK_ID,
            region=GCP_REGION,
            project_id=GCP_PROJECT,
            cluster_config=CONFIG,
            labels=LABELS,
            cluster_name=CLUSTER_NAME,
            gcp_conn_id=GCP_CONN_ID,
            retry=RETRY,