### 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/cli/commands/dag_command.py
dag = dag.partial_subset(
            task_ids_or_regex=args.task_regex, include_upstream=not args.ignore_dependencies
        )
        if not dag.task_dict:
            raise AirflowException(
                f"There are no tasks that match '{args.task_regex}' regex. Nothing to run, exiting..."
            )
    run_conf = None
    if args.conf:
        run_conf = json.loads(args.conf)
    if args.dry_run:
        print(f"Dry run of DAG {args.dag_id} on {args.start_date}")
        dr = DagRun(dag.dag_id, execution_date=args.start_date)
        for task in dag.tasks:
            print(f"Task {task.task_id}")
            ti = TaskInstance(task, run_id=None)
            ti.dag_run = dr
            ti.dry_run()
    else:
        if args.reset_dagruns:
            DAG.clear_dags(
                [dag],
                start_date=args.start_date,
                end_date=args.end_date,
                confirm_prompt=not args.yes,
                include_subdags=True,
                dag_run_state=State.NONE,
            )
        try:
            dag.run(