**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
session = settings.Session()
        pool = Pool(pool='test_scheduler_verify_pool_full_2_slots_per_task', slots=6)
        session.add(pool)
        session.commit()
        dag = SerializedDAG.from_dict(SerializedDAG.to_dict(dag))
        scheduler = SchedulerJob(executor=self.null_exec)
        scheduler.processor_agent = mock.MagicMock()
        
        date = DEFAULT_DATE
        for _ in range(5):
            dr = dag.create_dagrun(
                run_type=DagRunType.SCHEDULED,
                execution_date=date,
                state=State.RUNNING,
            )
            scheduler._schedule_dag_run(dr, 0, session)
            date = dag.following_schedule(date)
        task_instances_list = scheduler._executable_task_instances_to_queued(max_tis=32, session=session)
        
        self.assertEqual(len(task_instances_list), 3)
    def test_scheduler_verify_priority_and_slots(self):
        """
        Test task instances with higher priority are not queued
        when pool does not have enough slots.
        Though tasks with lower priority might be executed.
        """
        dag = DAG(
            dag_id='test_scheduler_verify_priority_and_slots',
            start_date=DEFAULT_DATE)