### 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)

filter_ = "filter"
        mock_cluster_client = AsyncMock(ClusterControllerAsyncClient)
        mock_client.return_value = mock_cluster_client
        await self.hook.list_clusters(project_id=GCP_PROJECT, region=GCP_LOCATION, filter_=filter_)
        mock_client.assert_called_once_with(region=GCP_LOCATION)
        mock_client.return_value.list_clusters.assert_called_once_with(
            request=dict(
                project_id=GCP_PROJECT,
                region=GCP_LOCATION,
                filter=filter_,
                page_size=None,
            ),
            metadata=(),
            retry=DEFAULT,
            timeout=None,
        )
    @pytest.mark.asyncio
    @async_mock.patch(DATAPROC_STRING.format("DataprocAsyncHook.get_cluster_client"))
    async def test_update_cluster(self, mock_client):