**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
dag = DagBag(read_dags_from_db=True, include_examples=False).get_dag(dag_id)
        
        dag.clear()
        dr = dag.create_dagrun(run_type=DagRunType.SCHEDULED,
                               state=State.FAILED,
                               execution_date=DEFAULT_DATE + timedelta(days=1),
                               start_date=DEFAULT_DATE + timedelta(days=1),
                               session=session)
        ti = dr.get_task_instance(task_id=op1.task_id, session=session)
        ti.state = initial_task_state
        session.commit()
        
        
        
        scheduler = SchedulerJob(num_runs=0, processor_poll_interval=0)
        executor = MockExecutor(do_update=False)
        executor.queued_tasks
        scheduler.executor = executor
        processor = mock.MagicMock()
        processor.done = True
        scheduler.processor_agent = processor
        scheduler._run_scheduler_loop()
        ti = dr.get_task_instance(task_id=op1.task_id, session=session)
        self.assertEqual(ti.state, expected_task_state)
        self.assertIsNotNone(ti.start_date)
        if expected_task_state in State.finished:
            self.assertIsNotNone(ti.end_date)
            self.assertEqual(ti.start_date, ti.end_date)
            self.assertIsNotNone(ti.duration)
    def test_dagrun_timeout_verify_max_active_runs(self):