### 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)

main = "main"
        self.builder.set_main(main_class=None, main_jar=main)
        assert main == self.builder.job["job"][self.job_type]["main_jar_file_uri"]
    def test_set_python_main(self):
        main = "main"
        self.builder.set_python_main(main)
        assert main == self.builder.job["job"][self.job_type]["main_python_file_uri"]
    @pytest.mark.parametrize(
        "job_name",
        [
            pytest.param("name", id="simple"),
            pytest.param("name_with_dash", id="name with underscores"),
            pytest.param("group.name", id="name with dot"),
            pytest.param("group.name_with_dash", id="name with dot and underscores"),
        ],
    )
    @mock.patch(DATAPROC_STRING.format("uuid.uuid4"))
    def test_set_job_name(self, mock_uuid, job_name):
        uuid = "test_uuid"
        expected_job_name = f"{job_name}_{uuid[:8]}".replace(".", "_")
        mock_uuid.return_value = uuid
        self.builder.set_job_name(job_name)
        assert expected_job_name == self.builder.job["job"]["reference"]["job_id"]
        assert len(self.builder.job["job"]["reference"]["job_id"]) == len(job_name) + 9
    def test_build(self):
        assert self.builder.job == self.builder.build()