**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
"""
    These tests are designed to detect changes in the number of queries for
    different DAG files. These tests allow easy detection when a change is
    made that affects the performance of the SchedulerJob.
    """
    def setUp(self) -> None:
        clear_db_runs()
        clear_db_pools()
        clear_db_dags()
        clear_db_sla_miss()
        clear_db_errors()
        clear_db_serialized_dags()
        clear_db_dags()
    @parameterized.expand(
        [
            
            
            
            (23, 1, 1),  
            
            (23, 1, 5),  
            
            (95, 10, 10),  
        ]
    )
    @pytest.mark.quarantined
    def test_execute_queries_count_with_harvested_dags(self, expected_query_count, dag_count, task_count):