### 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
dag_run = self._get_dag_run(dagrun_info, dag, session=session)
                tis_map = self._task_instances_for_dag_run(dag_run, session=session)
                if dag_run is None:
                    continue
                ti_status.active_runs.append(dag_run)
                ti_status.to_run.update(tis_map or {})
        processed_dag_run_dates = self._process_backfill_task_instances(
            ti_status=ti_status,
            executor=executor,
            pickle_id=pickle_id,
            start_date=start_date,
            session=session,
        )
        ti_status.executed_dag_run_dates.update(processed_dag_run_dates)
    @provide_session
    def _set_unfinished_dag_runs_to_failed(self, dag_runs, session=None):
        """
        Go through the dag_runs and update the state based on the task_instance state.
        Then set DAG runs that are not finished to failed.
        :param dag_runs: DAG runs
        :param session: session
        :return: None
        """
        for dag_run in dag_runs:
            dag_run.update_state()
            if dag_run.state not in State.finished:
                dag_run.set_state(State.FAILED)
            session.merge(dag_run)
    @provide_session
    def _execute(self, session=None):