### 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())
...
```
"""Updates states of the tasks."""
        self.log.debug("Inquiring about %s celery task(s)", len(self.tasks))
        state_and_info_by_celery_task_id = self.bulk_state_fetcher.get_many(self.tasks.values())
        self.log.debug("Inquiries completed.")
        for key, async_result in list(self.tasks.items()):
            state, info = state_and_info_by_celery_task_id.get(async_result.task_id)
            if state:
                self.update_task_state(key, state, info)
    def change_state(self, key: TaskInstanceKey, state: str, info=None) -> None:
        super().change_state(key, state, info)
        self.tasks.pop(key, None)
    def update_task_state(self, key: TaskInstanceKey, state: str, info: Any) -> None:
        """Updates state of a single task."""
        try:
            if state == celery_states.SUCCESS:
                self.success(key, info)
            elif state in (celery_states.FAILURE, celery_states.REVOKED):
                self.fail(key, info)
            elif state == celery_states.STARTED:
                pass
            elif state == celery_states.PENDING:
                pass
            else:
                self.log.info("Unexpected state for %s: %s", key, state)
        except Exception:
            self.log.exception("Error syncing the Celery executor, ignoring it.")
    def end(self, synchronous: bool = False) -> None: