### 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())
...
```
value=num_queued_tasks,
            tags={"status": "queued", "name": self.__class__.__name__},
        )
        Stats.gauge(
            "executor.running_tasks",
            value=num_running_tasks,
            tags={"status": "running", "name": self.__class__.__name__},
        )
        self.trigger_tasks(open_slots)
        
        self.log.debug("Calling the %s sync method", self.__class__)
        self.sync()
    def order_queued_tasks_by_priority(self) -> list[tuple[TaskInstanceKey, QueuedTaskInstanceType]]:
        """
        Orders the queued tasks by priority.
        :return: List of tuples from the queued_tasks according to the priority.
        """
        return sorted(
            self.queued_tasks.items(),
            key=lambda x: x[1][1],
            reverse=True,
        )
    def trigger_tasks(self, open_slots: int) -> None:
        """
        Initiates async execution of the queued tasks, up to the number of available slots.
        :param open_slots: Number of open slots
        """
        sorted_queue = self.order_queued_tasks_by_priority()
        task_tuples = []
        for _ in range(min((open_slots, len(self.queued_tasks)))):