Currently, the celery executor imports the `Task` model for the Celery database backend directly:

https://github.com/apache/airflow/blob/2dd4e96045d4a7f45cc8c06df3d25c4f1479392c/airflow/executors/celery_executor.py#L38

However, Celery, being highly customisable, uses `self.task_cls` in the backend implementation; this defaults to either `Task` or `TaskExtended` depending on the Celery configuration, see [`celery.backends.database:66`](https://github.com/celery/celery/blob/406f04a082949ac42ec7a4af94fed896c515aaa4/celery/backends/database/__init__.py#L66):

> ```python
>     task_cls = Task
> ```

and [`celery.backends.database:76,77`](https://github.com/celery/celery/blob/406f04a082949ac42ec7a4af94fed896c515aaa4/celery/backends/database/__init__.py#L76-L77):

> ```python
>         if self.extended_result:
>             self.task_cls = TaskExtended
> ```

Airflow should support this flexibility, and instead of importing a fixed class just use `backend.task_cls`, so in `_get_many_from_db_backend` just use:

```python
        Task = app.backend.task_cls
        with session_cleanup(session):
            tasks = session.query(Task).filter(Task.task_id.in_(task_ids)).all()
```

I suppose there is one downside to this: `TaskExtended` fetches a few more columns per task, but given that the result of `task.to_dict()` is passed to `backend.meta_from_decoded()`, which could also have been customised to make use of information contained in a custom model, that's neither here nor there. It would be the price anyone using the `result_extended` option would need to pay elsewhere anyway.
airflow/executors/celery_executor.py
super().change_state(key, state, info)
        self.tasks.pop(key, None)
        self.adopted_task_timeouts.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:
                
                self.adopted_task_timeouts.pop(key, None)
            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:
        if synchronous:
            while any(task.state not in celery_states.READY_STATES for task in self.tasks.values()):
                time.sleep(5)
        self.sync()
    def execute_async(