**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.
tests/jobs/test_scheduler_job.py
try:
                ti.run(ignore_ti_state=ignore_ti_state)
            except AirflowException:
                pass
        self.assertEqual(ti.try_number, 1)
        
        
        
        
        
        run_with_error(ti, ignore_ti_state=True)
        self.assertEqual(ti.state, State.UP_FOR_RETRY)
        self.assertEqual(ti.try_number, 2)
        with create_session() as session:
            ti.refresh_from_db(lock_for_update=True, session=session)
            ti.state = State.SCHEDULED
            session.merge(ti)
        
        executor.do_update = True
        do_schedule()  
        ti.refresh_from_db()
        self.assertEqual(ti.state, State.SUCCESS)
    @pytest.mark.quarantined
    def test_retry_handling_job(self):