### Apache Airflow version

2.2.0b2 (beta snapshot)

### Operating System

ubuntu 20.04

### Versions of Apache Airflow Providers

n/a

### Deployment

Virtualenv installation

### Deployment details

pip install /path/to/airflow/src

### What happened

Given any DAG initialized with: `schedule_interval=None`

Run `airflow dags test mydagname $(date +%Y-%m-%d)` and get an error:

```
INFO - No run dates were found for the given dates and dag interval.
```

This behavior changed in https://github.com/apache/airflow/pull/15397, it used to trigger a backfill dagrun at the given date.

### What you expected to happen

I expected a backfill dagrun with the given date, regardless of whether it fit into the `schedule_interval`.

If AIP-39 made that an unrealistic expectation, then I'd hope for some way to define unscheduled dags which can still be tested from the command line (which, so far as I know, is the fastest way to iterate on a DAG.).

As it is, I keep changing `schedule_interval` back and forth depending on whether I want to iterate via `astro dev start` (which tolerates `None` but does superfluous work if the dag is scheduled) or via `airflow dags test ...` (which doesn't tolerate `None`).

### How to reproduce

Initialize a DAG with: `schedule_interval=None` and run it via `airflow dags test mydagname $(date +%Y-%m-%d)`

### 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
"""
        Triggers the appropriate callback depending on the value of success, namely the
        on_failure_callback or on_success_callback. This method gets the context of a
        single TaskInstance part of this DagRun and passes that to the callable along
        with a 'reason', primarily to differentiate DagRun failures.
        .. note: The logs end up in
            ``$AIRFLOW_HOME/logs/scheduler/latest/PROJECT/DAG_FILE.py.log``
        :param dagrun: DagRun object
        :param success: Flag to specify if failure or success callback should be called
        :param reason: Completion reason
        :param session: Database session
        """
        callback = self.on_success_callback if success else self.on_failure_callback
        if callback:
            self.log.info('Executing dag callback function: %s', callback)
            tis = dagrun.get_task_instances(session=session)
            ti = tis[-1]  
            ti.task = self.get_task(ti.task_id)
            context = ti.get_template_context(session=session)
            context.update({'reason': reason})
            try:
                callback(context)
            except Exception:
                self.log.exception("failed to invoke dag state update callback")
                Stats.incr("dag.callback_exceptions")
    def get_active_runs(self):