### 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/jobs/backfill_job.py
"""
        Returns a map of task instance key to task instance object for the tasks to
        run in the given dag run.
        :param dag_run: the dag run to get the tasks from
        :type dag_run: airflow.models.DagRun
        :param session: the database session object
        :type session: sqlalchemy.orm.session.Session
        """
        tasks_to_run = {}
        if dag_run is None:
            return tasks_to_run
        
        self.reset_state_for_orphaned_tasks(filter_by_dag_run=dag_run, session=session)
        
        dag_run.refresh_from_db()
        make_transient(dag_run)
        try:
            for ti in dag_run.get_task_instances():
                
                if ti.state == State.NONE:
                    ti.set_state(State.SCHEDULED, session=session)
                if ti.state != State.REMOVED:
                    tasks_to_run[ti.key] = ti
            session.commit()
        except Exception:
            session.rollback()
            raise
        return tasks_to_run
    def _log_progress(self, ti_status):