### 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)

"image_uri": "https://www.googleapis.com/compute/beta/projects/"
        "custom_image_project_id/global/images/family/custom_image_family",
    },
    "secondary_worker_config": {
        "num_instances": 4,
        "machine_type_uri": "projects/project_id/zones/zone/machineTypes/worker_machine_type",
        "disk_config": {"boot_disk_type": "worker_disk_type", "boot_disk_size_gb": 256},
        "is_preemptible": True,
    },
    "software_config": {"properties": {"properties": "data"}, "optional_components": ["optional_components"]},
    "lifecycle_config": {
        "idle_delete_ttl": {"seconds": 60},
        "auto_delete_time": "2019-09-12T00:00:00.000000Z",
    },
    "encryption_config": {"gce_pd_kms_key_name": "customer_managed_key"},
    "autoscaling_config": {"policy_uri": "autoscaling_policy"},
    "config_bucket": "storage_bucket",
    "initialization_actions": [
        {"executable_file": "init_actions_uris", "execution_timeout": {"seconds": 600}}
    ],
    "endpoint_config": {
        "enable_http_port_access": True,
    },
}
LABELS = {"labels": "data", "airflow-version": AIRFLOW_VERSION}
LABELS.update({"airflow-version": "v" + airflow_version.replace(".", "-").replace("+", "-")})
CLUSTER = {"project_id": "project_id", "cluster_name": CLUSTER_NAME, "config": CONFIG, "labels": LABELS}
UPDATE_MASK = {
    "paths": ["config.worker_config.num_instances", "config.secondary_worker_config.num_instances"]
}