### 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
"""Return nodes with no parents. These are first to execute and are called roots or root nodes."""
        return [task for task in self.tasks if not task.upstream_list]
    @property
    def leaves(self) -> List[BaseOperator]:
        """Return nodes with no children. These are last to execute and are called leaves or leaf nodes."""
        return [task for task in self.tasks if not task.downstream_list]
    def topological_sort(self, include_subdag_tasks: bool = False):
        """
        Sorts tasks in topographical order, such that a task comes after any of its
        upstream dependencies.
        Heavily inspired by:
        http://blog.jupo.org/2012/04/06/topological-sorting-acyclic-directed-graphs/
        :param include_subdag_tasks: whether to include tasks in subdags, default to False
        :return: list of tasks in topological order
        """
        from airflow.operators.subdag import SubDagOperator  
        
        graph_unsorted = OrderedDict((task.task_id, task) for task in self.tasks)
        graph_sorted = []  
        
        if len(self.tasks) == 0:
            return tuple(graph_sorted)