### Apache Airflow version

2.2.0 (latest released)

### Operating System

Mac

### Versions of Apache Airflow Providers

_No response_

### Deployment

Other Docker-based deployment

### Deployment details

_No response_

### What happened

Getting an exception when returning None from next_dagrun_info in a custom timetable. The timetable protocol says that when None is returned a DagRun will not happen but right now it throws an exception.

Exception:
```
Traceback (most recent call last):
  File "/home/airflow/.local/lib/python3.9/site-packages/airflow/jobs/scheduler_job.py", line 623, in _execute
    self._run_scheduler_loop()
  File "/home/airflow/.local/lib/python3.9/site-packages/airflow/jobs/scheduler_job.py", line 704, in _run_scheduler_loop
    num_queued_tis = self._do_scheduling(session)
  File "/home/airflow/.local/lib/python3.9/site-packages/airflow/jobs/scheduler_job.py", line 787, in _do_scheduling
    callback_to_run = self._schedule_dag_run(dag_run, session)
  File "/home/airflow/.local/lib/python3.9/site-packages/airflow/jobs/scheduler_job.py", line 1039, in _schedule_dag_run
    self._update_dag_next_dagruns(dag, dag_model, active_runs)
  File "/home/airflow/.local/lib/python3.9/site-packages/airflow/jobs/scheduler_job.py", line 930, in _update_dag_next_dagruns
    data_interval = dag.get_next_data_interval(dag_model)
  File "/home/airflow/.local/lib/python3.9/site-packages/airflow/models/dag.py", line 629, in get_next_data_interval
    return self.infer_automated_data_interval(dag_model.next_dagrun)
  File "/home/airflow/.local/lib/python3.9/site-packages/airflow/models/dag.py", line 671, in infer_automated_data_interval
    raise ValueError(f"Not a valid timetable: {self.timetable!r}")
ValueError: Not a valid timetable: <my_timetables.workday_timetable.WorkdayTimetable object at 0x7f42b1f02430>
```

### What you expected to happen

DagRun to not happen.

### How to reproduce

Create a custom timetable and return None in next_dagrun_info after a few DagRuns are created by that timetable

### Anything else

_No response_

### Are you willing to submit PR?

- [ ] 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)

airflow/models/dag.py
:param executor: The executor instance to run the tasks
        :type executor: airflow.executor.base_executor.BaseExecutor
        :param donot_pickle: True to avoid pickling DAG object and send to workers
        :type donot_pickle: bool
        :param ignore_task_deps: True to skip upstream tasks
        :type ignore_task_deps: bool
        :param ignore_first_depends_on_past: True to ignore depends_on_past
            dependencies for the first set of tasks only
        :type ignore_first_depends_on_past: bool
        :param pool: Resource pool to use
        :type pool: str
        :param delay_on_limit_secs: Time in seconds to wait before next attempt to run
            dag run when max_active_runs limit has been reached
        :type delay_on_limit_secs: float
        :param verbose: Make logging output more verbose
        :type verbose: bool
        :param conf: user defined dictionary passed from CLI
        :type conf: dict
        :param rerun_failed_tasks:
        :type: bool
        :param run_backwards:
        :type: bool
        :param run_at_least_once: If true, always run the DAG at least once even
            if no logical run exists within the time range.
        :type: bool
        """
        from airflow.jobs.backfill_job import BackfillJob
        if not executor and local:
            from airflow.executors.local_executor import LocalExecutor
            executor = LocalExecutor()