**Apache Airflow version**:
Airflow 2.0

**Environment**:
Ubuntu 20.04 (WSL on Windows 10)

- **OS** (e.g. from /etc/os-release):
VERSION="20.04.1 LTS (Focal Fossa)"

- **Kernel** (e.g. `uname -a`):
Linux XXX 4.19.128-microsoft-standard #1 SMP Tue Jun 23 12:58:10 UTC 2020 x86_64 x86_64 x86_64 GNU/Linux

**What happened**:
After successfully running a SUBDAG, clearing it (including downstream+recursive) doesn't trigger the inner tasks. Instead, the subdag is marked successful and the inner tasks all stay cleared and aren't re-run.

**What you expected to happen**:
Expected Clear with DownStream + Recursive to re-run all subdag tasks.

<!-- What do you think went wrong? -->

**How to reproduce it**:
1. Using a slightly modified version of https://airflow.apache.org/docs/apache-airflow/stable/concepts.html#subdags:

```python
from airflow import DAG
from airflow.example_dags.subdags.subdag import subdag
from airflow.operators.dummy import DummyOperator
from airflow.operators.subdag import SubDagOperator
from airflow.utils.dates import days_ago

def subdag(parent_dag_name, child_dag_name, args):
    dag_subdag = DAG(
        dag_id=f'{parent_dag_name}.{child_dag_name}',
        default_args=args,
        start_date=days_ago(2),
        schedule_interval=None,
    )

    for i in range(5):
        DummyOperator(
            task_id='{}-task-{}'.format(child_dag_name, i + 1),
            default_args=args,
            dag=dag_subdag,
        )

    return dag_subdag
	
DAG_NAME = 'example_subdag_operator'

args = {
    'owner': 'airflow',
}

dag = DAG(
    dag_id=DAG_NAME, default_args=args, start_date=days_ago(2), schedule_interval=None, tags=['example']
)

start = DummyOperator(
    task_id='start',
    dag=dag,
)

section_1 = SubDagOperator(
    task_id='section-1',
    subdag=subdag(DAG_NAME, 'section-1', args),
    dag=dag,
)

some_other_task = DummyOperator(
    task_id='some-other-task',
    dag=dag,
)

section_2 = SubDagOperator(
    task_id='section-2',
    subdag=subdag(DAG_NAME, 'section-2', args),
    dag=dag,
)

end = DummyOperator(
    task_id='end',
    dag=dag,
)

start >> section_1 >> some_other_task >> section_2 >> end
```
2. Run the subdag fully. 
3. Clear (with recursive/downstream) any of the SubDags. 
4. The Subdag will be marked successful, but if you zoom into the subdag, you'll see all the child tasks were not run.

airflow/models/dag.py
self, dag: DAG, most_recent_dag_run: Optional[pendulum.DateTime], active_runs_of_dag: int
    ) -> None:
        """
        Calculate ``next_dagrun`` and `next_dagrun_create_after``
        :param dag: The DAG object
        :param most_recent_dag_run: DateTime of most recent run of this dag, or none if not yet scheduled.
        :param active_runs_of_dag: Number of currently active runs of this dag
        """
        self.next_dagrun, self.next_dagrun_create_after = dag.next_dagrun_info(most_recent_dag_run)
        if dag.max_active_runs and active_runs_of_dag >= dag.max_active_runs:
            
            log.debug(
                "DAG %s is at (or above) max_active_runs (%d of %d), not creating any more runs",
                dag.dag_id,
                active_runs_of_dag,
                dag.max_active_runs,
            )
            self.next_dagrun_create_after = None
        log.info("Setting next_dagrun for %s to %s", dag.dag_id, self.next_dagrun)
def dag(*dag_args, **dag_kwargs):
    """
    Python dag decorator. Wraps a function into an Airflow DAG.
    Accepts kwargs for operator kwarg. Can be used to parametrize DAGs.
    :param dag_args: Arguments for DAG object
    :type dag_args: list
    :param dag_kwargs: Kwargs for DAG object.
    :type dag_kwargs: dict
    """
    def wrapper(f: Callable):