### 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)

self.optional_components = optional_components
        self.master_machine_type = master_machine_type
        self.master_disk_type = master_disk_type
        self.master_disk_size = master_disk_size
        self.autoscaling_policy = autoscaling_policy
        self.worker_machine_type = worker_machine_type
        self.worker_disk_type = worker_disk_type
        self.worker_disk_size = worker_disk_size
        self.zone = zone
        self.network_uri = network_uri
        self.subnetwork_uri = subnetwork_uri
        self.internal_ip_only = internal_ip_only
        self.tags = tags
        self.service_account = service_account
        self.service_account_scopes = service_account_scopes
        self.idle_delete_ttl = idle_delete_ttl
        self.auto_delete_time = auto_delete_time
        self.auto_delete_ttl = auto_delete_ttl
        self.customer_managed_key = customer_managed_key
        self.enable_component_gateway = enable_component_gateway
        self.single_node = num_workers == 0
        if self.custom_image and self.image_version:
            raise ValueError("The custom_image and image_version can't be both set")
        if self.custom_image_family and self.image_version:
            raise ValueError("The image_version and custom_image_family can't be both set")
        if self.custom_image_family and self.custom_image:
            raise ValueError("The custom_image and custom_image_family can't be both set")
        if self.single_node and self.num_preemptible_workers > 0:
            raise ValueError("Single node cannot have preemptible workers.")
    def _get_init_action_timeout(self) -> dict: