**Apache Airflow version**: 2.0.0


**Kubernetes version (if you are using kubernetes)** (use `kubectl version`):

**Environment**:

- **Cloud provider or hardware configuration**:
- **OS** (e.g. from /etc/os-release):
- **Kernel** (e.g. `uname -a`):
- **Install tools**:
- **Others**:

**What happened**:

I have the following DAG:

```py
from datetime import datetime, timedelta

from airflow import DAG
from airflow.operators.bash_operator import BashOperator

with DAG(
    dag_id="sla_trigger",
    schedule_interval="* * * * *",
    start_date=datetime(2020, 2, 3),
) as dag:
    BashOperator(
        task_id="bash_task",
        bash_command="sleep 30",
        sla=timedelta(seconds=2),
    )

```

And in my understanding this dag should result in SLA miss every time it is triggered (every minute). However, after few minutes of running I don't see any SLA misses...

**What you expected to happen**:

I expect to see SLA if task takes longer than expected.

**How to reproduce it**:

Use the dag from above.

**Anything else we need to know**:

N/A

tests/jobs/test_scheduler_job.py
"""
        Test that the scheduler can successfully queue multiple dags in parallel
        when using "spawn" mode of multiprocessing. (Fork is default on Linux and older OSX)
        """
        dag_ids = ['test_start_date_scheduling', 'test_dagrun_states_success']
        for dag_id in dag_ids:
            dag = self.dagbag.get_dag(dag_id)
            dag.clear()
        scheduler = SchedulerJob(
            executor=self.null_exec,
            subdir=os.path.join(TEST_DAG_FOLDER, 'test_scheduler_dags.py'),
            num_runs=1,
        )
        scheduler.run()
        
        dag_id = 'test_start_date_scheduling'
        with create_session() as session:
            assert session.query(TaskInstance).filter(TaskInstance.dag_id == dag_id).count() == 0
    def test_scheduler_verify_pool_full(self):