**Description**

For a DAG that looks like this:

```
A >> B
```

If A fails, B goes into "upstream_failed" state. If a user then marks A "success", B will remain in "upstream_failed" state. It will not automatically start running. This scenario often happens if the failure of A is dealt with outside of Airflow and the user does not want Airflow to run A again. But he usually expect Airflow to run B after A is marked "success".

**Use case / motivation**

After A is marked "success", its downstream tasks B should be cleared automatically and get ready to be scheduled. To avoid changing this behaviour completely, there can be a toggle button next to "Success" that lets the user decide if he wants Airflow to automatically clear downstream tasks that are in "upstream_failed" state.
airflow/models/dag.py
"""
        Return (and lock) a list of Dag objects that are due to create a new DagRun.
        This will return a resultset of rows  that is row-level-locked with a "SELECT ... FOR UPDATE" query,
        you should ensure that any scheduling decisions are made in a single transaction -- as soon as the
        transaction is committed it will be unlocked.
        """
        
        
        
        query = (
            session.query(cls)
            .filter(
                cls.is_paused == expression.false(),
                cls.is_active == expression.true(),
                cls.next_dagrun_create_after <= func.now(),
            )
            .order_by(cls.next_dagrun_create_after)
            .limit(cls.NUM_DAGS_PER_DAGRUN_QUERY)
        )
        return with_row_locks(query, of=cls, session=session, **skip_locked(session=session))
    def calculate_dagrun_date_fields(