### 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
filter_for_tis = TI.filter_for_tis(tis_to_be_scheduled)
            session.query(TI).filter(filter_for_tis).update(
                values={TI.state: State.SCHEDULED}, synchronize_session=False
            )
    def _manage_executor_state(self, running):
        """
        Checks if the executor agrees with the state of task instances
        that are running
        :param running: dict of key, task to verify
        """
        executor = self.executor
        
        for key, value in list(executor.get_event_buffer().items()):
            state, info = value
            if key not in running:
                self.log.warning("%s state %s not in running=%s", key, state, running.values())
                continue
            ti = running[key]
            ti.refresh_from_db()
            self.log.debug("Executor state: %s task %s", state, ti)
            if state in (State.FAILED, State.SUCCESS) and ti.state in self.STATES_COUNT_AS_RUNNING:
                msg = (
                    "Executor reports task instance {} finished ({}) "
                    "although the task says its {}. Was the task "
                    "killed externally? Info: {}".format(ti, state, ti.state, info)
                )
                self.log.error(msg)
                ti.handle_failure_with_callback(error=msg)
    @provide_session
    def _get_dag_run(self, dagrun_info: DagRunInfo, dag: DAG, session: Session = None):