### 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
:param creating_job_id: id of the job creating this DagRun
        :type creating_job_id: int
        :param session: database session
        :type session: sqlalchemy.orm.session.Session
        :param dag_hash: Hash of Serialized DAG
        :type dag_hash: str
        :param data_interval: Data interval of the DagRun
        :type data_interval: tuple[datetime, datetime] | None
        """
        if run_id:  
            if not isinstance(run_id, str):
                raise ValueError(f"`run_id` expected to be a str is {type(run_id)}")
            if not run_type:
                run_type = DagRunType.from_run_id(run_id)
        elif run_type and execution_date is not None:  
            if not isinstance(run_type, DagRunType):
                raise ValueError(f"`run_type` expected to be a DagRunType is {type(run_type)}")
            run_id = DagRun.generate_run_id(run_type, execution_date)
        else:
            raise AirflowException(
                "Creating DagRun needs either `run_id` or both `run_type` and `execution_date`"
            )
        logical_date = timezone.coerce_datetime(execution_date)
        if data_interval is None and logical_date is not None:
            warnings.warn(
                "Calling `DAG.create_dagrun()` without an explicit data interval is deprecated",
                DeprecationWarning,
                stacklevel=3,
            )
            if run_type == DagRunType.MANUAL: