**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

airflow/jobs/scheduler_job.py
continue
            active_runs_of_dag = active_runs_of_dags.get(dag.dag_id, 0)
            if dag.max_active_runs and active_runs_of_dag >= dag.max_active_runs:
                self.log.info(
                    "DAG %s is at (or above) max_active_runs (%d of %d), not creating any more runs",
                    dag.dag_id,
                    active_runs_of_dag,
                    dag.max_active_runs,
                )
                dag_model.next_dagrun_create_after = None
            else:
                dag_model.next_dagrun, dag_model.next_dagrun_create_after = dag.next_dagrun_info(
                    dag_model.next_dagrun
                )
    def _schedule_dag_run(
        self,
        dag_run: DagRun,
        currently_active_runs: Set[datetime.datetime],
        session: Session,
    ) -> int:
        """
        Make scheduling decisions about an individual dag run
        ``currently_active_runs`` is passed in so that a batch query can be
        used to ask this for all dag runs in the batch, to avoid an n+1 query.
        :param dag_run: The DagRun to schedule
        :param currently_active_runs: Number of currently active runs of this DAG
        :return: Number of tasks scheduled
        """
        dag = dag_run.dag = self.dagbag.get_dag(dag_run.dag_id, session=session)
        if not dag: