**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
"""
        Test that the dag file processor will not crash when trying to send
        sla miss notification for a deleted task
        """
        session = settings.Session()
        test_start_date = days_ago(2)
        dag = DAG(dag_id='test_sla_miss',
                  default_args={'start_date': test_start_date,
                                'sla': datetime.timedelta(days=1)})
        task = DummyOperator(task_id='dummy',
                             dag=dag,
                             owner='airflow',
                             email='test@test.com',
                             sla=datetime.timedelta(hours=1))
        session.merge(TaskInstance(task=task, execution_date=test_start_date, state='Success'))
        
        session.merge(SlaMiss(task_id='dummy_deleted', dag_id='test_sla_miss',
                              execution_date=test_start_date))
        mock_log = mock.MagicMock()
        dag_file_processor = DagFileProcessor(dag_ids=[], log=mock_log)
        dag_file_processor.manage_slas(dag=dag, session=session)
    @parameterized.expand([
        [State.NONE, None, None],
        [State.UP_FOR_RETRY, timezone.utcnow() - datetime.timedelta(minutes=30),
         timezone.utcnow() - datetime.timedelta(minutes=15)],
        [State.UP_FOR_RESCHEDULE, timezone.utcnow() - datetime.timedelta(minutes=30),
         timezone.utcnow() - datetime.timedelta(minutes=15)],
    ])
    def test_dag_file_processor_process_task_instances(self, state, start_date, end_date):