### 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
"""Infer a data interval for a run against this DAG.
        This method is used to bridge runs created prior to AIP-39
        implementation, which do not have an explicit data interval. Therefore,
        this method only considers ``schedule_interval`` values valid prior to
        Airflow 2.2.
        DO NOT use this method is there is a known data interval.
        """
        timetable_type = type(self.timetable)
        if issubclass(timetable_type, (NullTimetable, OnceTimetable)):
            return DataInterval.exact(timezone.coerce_datetime(logical_date))
        start = timezone.coerce_datetime(logical_date)
        if issubclass(timetable_type, CronDataIntervalTimetable):
            end = cast(CronDataIntervalTimetable, self.timetable)._get_next(start)
        elif issubclass(timetable_type, DeltaDataIntervalTimetable):
            end = cast(DeltaDataIntervalTimetable, self.timetable)._get_next(start)
        else:
            raise ValueError(f"Not a valid timetable: {self.timetable!r}")
        return DataInterval(start, end)
    def next_dagrun_info(