**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
{
                        models.TaskInstance.end_date: current_time,
                        models.TaskInstance.duration: 0,
                    }
                )
            tis_changed = (
                session.query(models.TaskInstance)
                .filter(
                    models.TaskInstance.dag_id == subq.c.dag_id,
                    models.TaskInstance.task_id == subq.c.task_id,
                    models.TaskInstance.execution_date == subq.c.execution_date,
                )
                .update(ti_prop_update, synchronize_session=False)
            )
        if tis_changed > 0:
            session.flush()
            self.log.warning(
                "Set %s task instances to state=%s as their associated DagRun was not in RUNNING state",
                tis_changed,
                new_state,
            )
            Stats.gauge('scheduler.tasks.without_dagrun', tis_changed)
    @provide_session
    def __get_concurrency_maps(
        self, states: List[str], session: Session = None
    ) -> Tuple[DefaultDict[str, int], DefaultDict[Tuple[str, str], int]]:
        """
        Get the concurrency maps.
        :param states: List of states to query for
        :type states: list[airflow.utils.state.State]