### Overview
Some Executors have their own first class CLI commands (now that’s hardcoding/coupling!) which setup or modify various components related to that Executor.
### Examples
- **5a**) Celery Executor commands: https://github.com/apache/airflow/blob/27e2101f6ee5567b2843cbccf1dca0b0e7c96186/airflow/cli/cli_parser.py#L1689-L1734
- **5b**) Kubernetes Executor commands: https://github.com/apache/airflow/blob/27e2101f6ee5567b2843cbccf1dca0b0e7c96186/airflow/cli/cli_parser.py#L1754-L1771
- **5c**) Default CLI parser has hardcoded logic for Celery and Kubernetes Executors specifically: https://github.com/apache/airflow/blob/27e2101f6ee5567b2843cbccf1dca0b0e7c96186/airflow/cli/cli_parser.py#L63-L99
### Proposal
Update the BaseExecutor interface with a pluggable mechanism to vend CLI `GroupCommands` and parsers. Executor subclasses would then implement these methods, if applicable, which would then be called to fetch commands and parsers from within Airflow Core cli parser code. We would then migrate the existing Executor CLI code from cli_parser to the respective Executor class.

Pseudo-code example for vending `GroupCommand`s:
```python
# Existing code in cli_parser.py
...
airflow_commands: List[CLICommand] = [
GroupCommand(
        name='dags',
        help='Manage DAGs',
        subcommands=DAGS_COMMANDS,
    ),
    ...
]
# New code to add groups vended by executor classes
executor_cls, _ = ExecutorLoader.import_executor_cls(conf.get('core', 'EXECUTOR'))
airflow_commands.append(executor_cls.get_cli_group_commands())
...
```
if TYPE_CHECKING:
            assert self.kube_client
            assert self.kube_scheduler
        readable_tis = []
        for ti in tis:
            selector = PodGenerator.build_selector_for_k8s_executor_pod(
                dag_id=ti.dag_id,
                task_id=ti.task_id,
                try_number=ti.try_number,
                map_index=ti.map_index,
                run_id=ti.run_id,
                airflow_worker=ti.queued_by_job_id,
            )
            namespace = self._get_pod_namespace(ti)
            pod_list = self.kube_client.list_namespaced_pod(
                namespace=namespace,
                label_selector=selector,
            ).items
            if not pod_list:
                self.log.warning("Cannot find pod for ti %s", ti)
                continue
            elif len(pod_list) > 1:
                self.log.warning("Found multiple pods for ti %s: %s", ti, pod_list)
                continue
            readable_tis.append(repr(ti))
            self.kube_scheduler.delete_pod(pod_name=pod_list[0].metadata.name, namespace=namespace)
        return readable_tis
    def adopt_launched_task(
        self,
        kube_client: client.CoreV1Api,