### Apache Airflow version

2.4.1

### What happened

When using dataset scheduling, it isn't obvious which datasets a downstream dataset consumer is awaiting in order for the DAG to be scheduled.

I would assume that this is supposed to be solved by the `Latest Update` column in the modal that opens when selecting `x of y datasets updated`, but it appears that the data isn't being populated.

<img width="601" alt="image" src="https://user-images.githubusercontent.com/5778047/194116186-d582cede-c778-47f7-8341-fc13a69a2358.png">

Although one of the datasets has been produced, there is no data in the `Latest Update` column of the modal.

In the above example, both datasets have been produced > 1 time.

<img width="581" alt="image" src="https://user-images.githubusercontent.com/5778047/194116368-ceff241f-a623-4893-beb7-637b821c4b53.png">

<img width="581" alt="image" src="https://user-images.githubusercontent.com/5778047/194116410-19045f5a-8400-47b0-afcb-9fbbffca26ee.png">


### What you think should happen instead

The `Latest Update` column should be populated with the latest update timestamp for each dataset required to schedule a downstream, dataset consuming DAG.

Ideally there would be some form of highlighting on the "missing" datasets for quick visual feedback when DAGs have a large number of datasets required for scheduling.

### How to reproduce

1. Create a DAG (or 2 individual DAGs) that produces 2 datasets
2. Produce both datasets
3. Then produce _only one_ dataset
4. Check the modal by clicking from the home screen on the `x of y datasets updated` button.

### Operating System

Debian GNU/Linux 11 (bullseye)

### Versions of Apache Airflow Providers

_No response_

### Deployment

Docker-Compose

### Deployment details

_No response_

### Anything else

_No response_

### Are you willing to submit PR?

- [ ] Yes I am willing to submit a PR!

### Code of Conduct

- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

session.query(TaskFail)
            .join(TaskFail.dag_run)
            .filter(
                TaskFail.dag_id == dag.dag_id,
                DagRun.execution_date >= min_date,
                DagRun.execution_date <= base_date,
            )
        )
        if dag.partial:
            ti_fails = ti_fails.filter(TaskFail.task_id.in_([t.task_id for t in dag.tasks]))
        fails_totals = defaultdict(int)
        for failed_task_instance in ti_fails:
            dict_key = (
                failed_task_instance.dag_id,
                failed_task_instance.task_id,
                failed_task_instance.run_id,
            )
            if failed_task_instance.duration:
                fails_totals[dict_key] += failed_task_instance.duration
        
        mapped_tis = set()
        tis_grouped = itertools.groupby(task_instances, lambda x: (x.dag_id, x.task_id, x.run_id))
        for key, tis in tis_grouped:
            tis = list(tis)
            duration = sum(x.duration for x in tis if x.duration)
            if duration:
                first_ti = tis[0]
                if first_ti.map_index >= 0:
                    mapped_tis.add(first_ti.task_id)
                date_time = wwwutils.epoch(first_ti.execution_date)