**Apache Airflow version**: 2.0.0a1

**What happened**:

On my dev environment I have a lot of DAG runs with failed tasks, where the *downstream* task instances then have been set to `upstream_failed`. The scheduler is now refusing to schedule more instances for such DAGs.

The scheduler looks for such task instances that are not in the `State.finished` set:

https://github.com/apache/airflow/blob/db3fe0926bb75008311eed804052c90bfa912424/airflow/jobs/scheduler_job.py#L1482-L1488

However, `State.finished` is currently not a proper inverse of *running* states, it only includes success, failed, and skipped:

https://github.com/apache/airflow/blob/db3fe0926bb75008311eed804052c90bfa912424/airflow/utils/state.py#L110-L114

What is missing is the task instance `upstream_failed` state, so currently my log is being filled (very rapidly) with:

```
[2020-10-18 13:22:18,503] {{scheduler_job.py:1658}} INFO - DAG test_dag already has 26 active runs, not queuing any more tasks
```

Those 26 'active runs' are simply dag runs that failed and have 'upstream_failed' task instances.

Either `upstream_failed` should be added to `State.finished`, or the Scheduler task instance test should explicitly take that state into account.
airflow/models/dagrun.py
return synonym('_state', descriptor=property(self.get_state, self.set_state))
    @provide_session
    def refresh_from_db(self, session: Session = None):
        """
        Reloads the current dagrun from the database
        :param session: database session
        :type session: Session
        """
        DR = DagRun
        exec_date = func.cast(self.execution_date, DateTime)
        dr = session.query(DR).filter(
            DR.dag_id == self.dag_id,
            func.cast(DR.execution_date, DateTime) == exec_date,
            DR.run_id == self.run_id
        ).one()
        self.id = dr.id
        self.state = dr.state
    @classmethod
    def next_dagruns_to_examine(
        cls,
        session: Session,
        max_number: Optional[int] = None,
    ):
        """
        Return the next DagRuns that the scheduler should attempt to schedule.
        This will return zero or more DagRun rows that are 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.