### 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)

from airflow.providers.google.cloud.hooks.dataproc import DataprocAsyncHook, DataprocHook, DataProcJobBuilder
from airflow.providers.google.common.consts import CLIENT_INFO
from airflow.version import version
from tests.providers.google.cloud.utils.compat import AsyncMock, async_mock
AIRFLOW_VERSION = "v" + version.replace(".", "-").replace("+", "-")
JOB = {"job": "test-job"}
JOB_ID = "test-id"
TASK_ID = "test-task-id"
GCP_LOCATION = "global"
GCP_PROJECT = "test-project"
CLUSTER_CONFIG = {"test": "test"}
LABELS = {"test": "test"}
CLUSTER_NAME = "cluster-name"
CLUSTER = {
    "cluster_name": CLUSTER_NAME,
    "config": CLUSTER_CONFIG,
    "labels": LABELS,
    "project_id": GCP_PROJECT,
}
BATCH = {"batch": "test-batch"}
BATCH_ID = "batch-id"
BATCH_NAME = "projects/{}/regions/{}/batches/{}"
PARENT = "projects/{}/regions/{}"
BASE_STRING = "airflow.providers.google.common.hooks.base_google.{}"
DATAPROC_STRING = "airflow.providers.google.cloud.hooks.dataproc.{}"
def mock_init(*args, **kwargs):
    pass
async def mock_awaitable(*args, **kwargs):
    pass
class TestDataprocHook: