**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 scheduler respects task start dates that are different from DAG start dates
        """
        dagbag = DagBag(dag_folder=os.path.join(settings.DAGS_FOLDER, "no_dags.py"), include_examples=False)
        dag_id = 'test_task_start_date_scheduling'
        dag = self.dagbag.get_dag(dag_id)
        dag.is_paused_upon_creation = False
        dagbag.bag_dag(dag=dag, root_dag=dag)
        
        other_dag = self.dagbag.get_dag('test_start_date_scheduling')
        other_dag.is_paused_upon_creation = True
        dagbag.bag_dag(dag=other_dag, root_dag=other_dag)
        dagbag.sync_to_db()
        scheduler = SchedulerJob(executor=self.null_exec,
                                 subdir=dag.fileloc,
                                 num_runs=2)
        scheduler.run()
        session = settings.Session()
        tiq = session.query(TaskInstance).filter(TaskInstance.dag_id == dag_id)
        ti1s = tiq.filter(TaskInstance.task_id == 'dummy1').all()
        ti2s = tiq.filter(TaskInstance.task_id == 'dummy2').all()
        self.assertEqual(len(ti1s), 0)
        self.assertEqual(len(ti2s), 2)
        for task in ti2s:
            self.assertEqual(task.state, State.SUCCESS)
    def test_scheduler_multiprocessing(self):