Inconsistent `time_elapsed_scanning_total` result across different queries scanning the same data

### Describe the bug

Initially, I thought `time_elapsed_scanning_total` is just the amount of CPU time spent in fetching and decoding the Parquet files, as described below:
https://github.com/apache/datafusion/blob/ebf49b4fe585820caaad3b8c04529633190f48be/docs/source/user-guide/explain-usage.md?plain=1#L211

[Then from this discussion](https://github.com/apache/datafusion/discussions/16572#discussioncomment-13607637), I found out that the `time_elapsed_scanning_total` may include the time of execution of the parent operator.
https://github.com/apache/datafusion/blob/9f3cc7b85b084102916acf489354d7f65f7c9e7c/datafusion/datasource/src/file_stream.rs#L463

#### Query 1
```
ProjectionExec: expr=[spanid@0 as spanid, traceid@1 as traceid, spanname@2 as spanname, scopename@3 as scopename, timestamp@4 as timestamp, servicename@5 as servicename, parentspanid@6 as parentspanid, duration@7 as duration, tags@8 as tags, spankind@9 as spankind, mlrepoid@10 as mlrepoid, tracingprojectid@11 as tracingprojectid, httpstatuscode@12 as httpstatuscode, matched@13 as matched], metrics=[output_rows=10, elapsed_compute=7.36µs]
SortPreservingMergeExec: [Timestamp@14 ASC NULLS LAST], fetch=10, metrics=[output_rows=10, elapsed_compute=59.961µs]
SortExec: TopK(fetch=10), expr=[Timestamp@14 ASC NULLS LAST], preserve_partitioning=[true], metrics=[output_rows=80, elapsed_compute=12.02048ms, row_replacements=799]
ProjectionExec: expr=[first_value(tbl_a568758dc0934c4498693bcb9754dca6.SpanId)@1 as spanid, first_value(tbl_a568758dc0934c4498693bcb9754dca6.TraceId)@2 as traceid, first_value(tbl_a568758dc0934c4498693bcb9754dca6.SpanName)@3 as spanname, first_value(tbl_a568758dc0934c4498693bcb9754dca6.ScopeName)@4 as scopename, first_value(tbl_a568758dc0934c4498693bcb9754dca6.Timestamp)@5 as timestamp, first_value(tbl_a568758dc0934c4498693bcb9754dca6.ServiceName)@6 as servicename, first_value(tbl_a568758dc0934c4498693bcb9754dca6.ParentSpanId)@7 as parentspanid, first_value(tbl_a568758dc0934c4498693bcb9754dca6.Duration)@8 as duration, first_value(tbl_a568758dc0934c4498693bcb9754dca6.Tags)@9 as tags, first_value(tbl_a568758dc0934c4498693bcb9754dca6.SpanKind)@10 as spankind, first_value(tbl_a568758dc0934c4498693bcb9754dca6.MlRepoId)@11 as mlrepoid, first_value(TracingProjectId)@12 as tracingprojectid, first_value(tbl_a568758dc0934c4498693bcb9754dca6.StatusCode)@13 as httpstatuscode, ((first_value(tbl_a568758dc0934c4498693bcb9754dca6.ServiceName)@6 = user-service OR first_value(tbl_a568758dc0934c4498693bcb9754dca6.ServiceName)@6 = auth-service) AND first_value(tbl_a568758dc0934c4498693bcb9754dca6.SpanName)@3 LIKE %login% AND first_value(tbl_a568758dc0934c4498693bcb9754dca6.Duration)@8 >= 1000000 AND first_value(tbl_a568758dc0934c4498693bcb9754dca6.Duration)@8 <= 5000000 AND first_value(tbl_a568758dc0934c4498693bcb9754dca6.StatusCode)@13 >= 200 AND first_value(tbl_a568758dc0934c4498693bcb9754dca6.StatusCode)@13 <= 299) IS NOT DISTINCT FROM true as matched, first_value(tbl_a568758dc0934c4498693bcb9754dca6.Timestamp)@5 as Timestamp], metrics=[output_rows=492435, elapsed_compute=1.679348ms]
AggregateExec: mode=FinalPartitioned, gby=[SpanId@0 as SpanId], aggr=[first_value(tbl_a568758dc0934c4498693bcb9754dca6.SpanId), first_value(tbl_a568758dc0934c4498693bcb9754dca6.TraceId), first_value(tbl_a568758dc0934c4498693bcb9754dca6.SpanName), first_value(tbl_a568758dc0934c4498693bcb9754dca6.ScopeName), first_value(tbl_a568758dc0934c4498693bcb9754dca6.Timestamp), first_value(tbl_a568758dc0934c4498693bcb9754dca6.ServiceName), first_value(tbl_a568758dc0934c4498693bcb9754dca6.ParentSpanId), first_value(tbl_a568758dc0934c4498693bcb9754dca6.Duration), first_value(tbl_a568758dc0934c4498693bcb9754dca6.Tags), first_value(tbl_a568758dc0934c4498693bcb9754dca6.SpanKind), first_value(tbl_a568758dc0934c4498693bcb9754dca6.MlRepoId), first_value(TracingProjectId), first_value(tbl_a568758dc0934c4498693bcb9754dca6.StatusCode)], metrics=[output_rows=492435, elapsed_compute=3.89974745s, spill_count=0, spilled_bytes=0, spilled_rows=0, peak_mem_used=1391310907]
CoalesceBatchesExec: target_batch_size=20000, metrics=[output_rows=492435, elapsed_compute=954.019µs]
RepartitionExec: partitioning=Hash([SpanId@0], 8), input_partitions=8, metrics=[fetch_time=3.035408986s, repartition_time=74.415606ms, send_time=165.047905ms]
AggregateExec: mode=Partial, gby=[SpanId@2 as SpanId], aggr=[first_value(tbl_a568758dc0934c4498693bcb9754dca6.SpanId), first_value(tbl_a568758dc0934c4498693bcb9754dca6.TraceId), first_value(tbl_a568758dc0934c4498693bcb9754dca6.SpanName), first_value(tbl_a568758dc0934c4498693bcb9754dca6.ScopeName), first_value(tbl_a568758dc0934c4498693bcb9754dca6.Timestamp), first_value(tbl_a568758dc0934c4498693bcb9754dca6.ServiceName), first_value(tbl_a568758dc0934c4498693bcb9754dca6.ParentSpanId), first_value(tbl_a568758dc0934c4498693bcb9754dca6.Duration), first_value(tbl_a568758dc0934c4498693bcb9754dca6.Tags), first_value(tbl_a568758dc0934c4498693bcb9754dca6.SpanKind), first_value(tbl_a568758dc0934c4498693bcb9754dca6.MlRepoId), first_value(TracingProjectId), first_value(tbl_a568758dc0934c4498693bcb9754dca6.StatusCode)], metrics=[output_rows=492435, elapsed_compute=2.378294013s, spill_count=0, spilled_bytes=0, spilled_rows=0, skipped_aggregation_rows=270299, peak_mem_used=633360309]
CoalesceBatchesExec: target_batch_size=20000, metrics=[output_rows=492435, elapsed_compute=63.812µs]
FilterExec: TraceId@2 = fb09d0c9b49136bb161464b3e32c5083 AND ParentSpanId@4 = 662b7388122dfb79 AND Timestamp@0 >= 1744702200000000 AND TsBucketStart@1 >= 1745712000, projection=[Timestamp@0, TraceId@2, SpanId@3, ParentSpanId@4, ServiceName@5, SpanName@6, SpanKind@7, ScopeName@8, Duration@9, StatusCode@10, Tags@11, MlRepoId@12, TracingProjectId@13], metrics=[output_rows=492435, elapsed_compute=3.821363ms]
DeltaScan, metrics=[files_pruned=96, files_scanned=205]
DataSourceExec: file_groups={8 groups: [[...]]}, projection=[...], file_type=parquet, predicate=TraceId@2 = fb09d0c9b49136bb161464b3e32c5083 AND ParentSpanId@4 = 662b7388122dfb79 AND Timestamp@0 >= 1744702200000000 AND TsBucketStart@1 >= 1745712000, pruning_predicate=TraceId_null_count@2 != row_count@3 AND TraceId_min@0 <= fb09d0c9b49136bb161464b3e32c5083 AND fb09d0c9b49136bb161464b3e32c5083 <= TraceId_max@1 AND ParentSpanId_null_count@6 != row_count@3 AND ParentSpanId_min@4 <= 662b7388122dfb79 AND 662b7388122dfb79 <= ParentSpanId_max@5 AND Timestamp_null_count@8 != row_count@3 AND Timestamp_max@7 >= 1744702200000000 AND TsBucketStart_null_count@10 != row_count@3 AND TsBucketStart_max@9 >= 1745712000, required_guarantees=[ParentSpanId in (662b7388122dfb79), TraceId in (fb09d0c9b49136bb161464b3e32c5083)], metrics=[output_rows=492435, elapsed_compute=8ns, bytes_scanned=369890183, file_open_errors=0, file_scan_errors=0, num_predicate_creation_errors=0, page_index_rows_matched=5863737, page_index_rows_pruned=0, predicate_evaluation_errors=0, pushdown_rows_matched=12712344, pushdown_rows_pruned=5371302, row_groups_matched_bloom_filter=10, row_groups_matched_statistics=205, row_groups_pruned_bloom_filter=195, row_groups_pruned_statistics=0, bloom_filter_eval_time=8.413459ms, metadata_load_time=73.751446ms, page_index_eval_time=297.417µs, row_pushdown_eval_time=28.275053ms, statistics_eval_time=4.391944ms, time_elapsed_opening=21.034126ms, time_elapsed_processing=593.548393ms, time_elapsed_scanning_total=3.42025892s, time_elapsed_scanning_until_data=395.516949ms]
```
* `output_rows=492435`
* `bytes_scanned=369890183`
* **`time_elapsed_scanning_total=3.42025892s`**

#### Query 2
```
ProjectionExec: expr=[spanid@0 as spanid, traceid@1 as traceid, spanname@2 as spanname, scopename@3 as scopename, timestamp@4 as timestamp, servicename@5 as servicename, parentspanid@6 as parentspanid, duration@7 as duration, tags@8 as tags, spankind@9 as spankind, mlrepoid@10 as mlrepoid, tracingprojectid@11 as tracingprojectid, httpstatuscode@12 as httpstatuscode, matched@13 as matched], metrics=[output_rows=10, elapsed_compute=2.43µs]
SortPreservingMergeExec: [Timestamp@14 ASC NULLS LAST], fetch=10, metrics=[output_rows=10, elapsed_compute=29.09µs]
SortExec: TopK(fetch=10), expr=[Timestamp@14 ASC NULLS LAST], preserve_partitioning=[true], metrics=[output_rows=80, elapsed_compute=12.585606ms, row_replacements=576]
ProjectionExec: expr=[SpanId@0 as spanid, TraceId@1 as traceid, SpanName@2 as spanname, ScopeName@3 as scopename, Timestamp@4 as timestamp, ServiceName@5 as servicename, ParentSpanId@6 as parentspanid, Duration@7 as duration, Tags@8 as tags, SpanKind@9 as spankind, MlRepoId@10 as mlrepoid, TracingProjectId@11 as tracingprojectid, StatusCode@12 as httpstatuscode, ((ServiceName@5 = user-service OR ServiceName@5 = auth-service) AND SpanName@2 LIKE %login% AND Duration@7 >= 1000000 AND Duration@7 <= 5000000 AND StatusCode@12 >= 200 AND StatusCode@12 <= 299) IS NOT DISTINCT FROM true as matched, Timestamp@4 as Timestamp], metrics=[output_rows=492435, elapsed_compute=1.655463ms]
AggregateExec: mode=FinalPartitioned, gby=[SpanId@0 as SpanId, TraceId@1 as TraceId, SpanName@2 as SpanName, ScopeName@3 as ScopeName, Timestamp@4 as Timestamp, ServiceName@5 as ServiceName, ParentSpanId@6 as ParentSpanId, Duration@7 as Duration, Tags@8 as Tags, SpanKind@9 as SpanKind, MlRepoId@10 as MlRepoId, TracingProjectId@11 as TracingProjectId, StatusCode@12 as StatusCode, TsBucketStart@13 as TsBucketStart], aggr=[], ordering_mode=PartiallySorted([1, 6]), metrics=[output_rows=492435, elapsed_compute=345.710556ms, spill_count=0, spilled_bytes=0, spilled_rows=0, peak_mem_used=233345876]
CoalesceBatchesExec: target_batch_size=20000, metrics=[output_rows=492435, elapsed_compute=631.275µs]
RepartitionExec: partitioning=Hash([SpanId@0, TraceId@1, SpanName@2, ScopeName@3, Timestamp@4, ServiceName@5, ParentSpanId@6, Duration@7, Tags@8, SpanKind@9, MlRepoId@10, TracingProjectId@11, StatusCode@12, TsBucketStart@13], 8), input_partitions=8, metrics=[fetch_time=954.934766ms, repartition_time=64.976695ms, send_time=16.871715ms]
AggregateExec: mode=Partial, gby=[SpanId@0 as SpanId, TraceId@1 as TraceId, SpanName@2 as SpanName, ScopeName@3 as ScopeName, Timestamp@4 as Timestamp, ServiceName@5 as ServiceName, ParentSpanId@6 as ParentSpanId, Duration@7 as Duration, Tags@8 as Tags, SpanKind@9 as SpanKind, MlRepoId@10 as MlRepoId, TracingProjectId@11 as TracingProjectId, StatusCode@12 as StatusCode, TsBucketStart@13 as TsBucketStart], aggr=[], ordering_mode=PartiallySorted([1, 6]), metrics=[output_rows=492435, elapsed_compute=345.95653ms, spill_count=0, spilled_bytes=0, spilled_rows=0, peak_mem_used=204458097]
ProjectionExec: expr=[SpanId@3 as SpanId, TraceId@2 as TraceId, SpanName@6 as SpanName, ScopeName@8 as ScopeName, Timestamp@0 as Timestamp, ServiceName@5 as ServiceName, ParentSpanId@4 as ParentSpanId, Duration@9 as Duration, Tags@11 as Tags, SpanKind@7 as SpanKind, MlRepoId@12 as MlRepoId, CAST(TracingProjectId@13 AS Utf8) as TracingProjectId, StatusCode@10 as StatusCode, TsBucketStart@1 as TsBucketStart], metrics=[output_rows=492435, elapsed_compute=4.419444ms]
CoalesceBatchesExec: target_batch_size=20000, metrics=[output_rows=492435, elapsed_compute=51.84µs]
FilterExec: TraceId@2 = fb09d0c9b49136bb161464b3e32c5083 AND ParentSpanId@4 = 662b7388122dfb79 AND Timestamp@0 >= 1744702200000000 AND TsBucketStart@1 >= 1745712000, metrics=[output_rows=492435, elapsed_compute=3.624526ms]
DeltaScan, metrics=[files_pruned=96, files_scanned=205]
DataSourceExec: file_groups={8 groups: [[...], ...]}, projection=[...], file_type=parquet, predicate=TraceId@2 = fb09d0c9b49136bb161464b3e32c5083 AND ParentSpanId@4 = 662b7388122dfb79 AND Timestamp@0 >= 1744702200000000 AND TsBucketStart@1 >= 1745712000, pruning_predicate=TraceId_null_count@2 != row_count@3 AND TraceId_min@0 <= fb09d0c9b49136bb161464b3e32c5083 AND fb09d0c9b49136bb161464b3e32c5083 <= TraceId_max@1 AND ParentSpanId_null_count@6 != row_count@3 AND ParentSpanId_min@4 <= 662b7388122dfb79 AND 662b7388122dfb79 <= ParentSpanId_max@5 AND Timestamp_null_count@8 != row_count@3 AND Timestamp_max@7 >= 1744702200000000 AND TsBucketStart_null_count@10 != row_count@3 AND TsBucketStart_max@9 >= 1745712000, required_guarantees=[ParentSpanId in (662b7388122dfb79), TraceId in (fb09d0c9b49136bb161464b3e32c5083)], metrics=[output_rows=492435, elapsed_compute=8ns, bytes_scanned=369887916, file_open_errors=0, file_scan_errors=0, num_predicate_creation_errors=0, page_index_rows_matched=5863737, page_index_rows_pruned=0, predicate_evaluation_errors=0, pushdown_rows_matched=12712344, pushdown_rows_pruned=5371302, row_groups_matched_bloom_filter=10, row_groups_matched_statistics=205, row_groups_pruned_bloom_filter=195, row_groups_pruned_statistics=0, bloom_filter_eval_time=8.39861ms, metadata_load_time=62.433084ms, page_index_eval_time=292.354µs, row_pushdown_eval_time=28.451684ms, statistics_eval_time=4.473339ms, time_elapsed_opening=8.687883ms, time_elapsed_processing=589.865242ms, time_elapsed_scanning_total=750.34081ms, time_elapsed_scanning_until_data=403.341742ms]
```
* `output_rows=492435`
* `bytes_scanned=369887916`
* **`time_elapsed_scanning_total=750.34081ms`**

As you can see, for similar output rows and data, the `time_elapsed_scanning_total` reports very different numbers.

What metric should I use to identify the latency introduced in the query response time due to Parquet I/O (getting from object store or disk), decompression, and deserialization?

### To Reproduce

_No response_

### Expected behavior

_No response_

### Additional context

I am using Datafusion 47.

Beginne mit der verlinkten Dokumentation zu den Metriken und der Metrikbehandlung in datafusion/datasource/src/file_stream.rs um Zeile 463. Vergleiche die beiden bereitgestellten Ausführungspläne und ihre DataSourceExec-Metriken, um zu bestimmen, welche Arbeit des übergeordneten Operators zu time_elapsed_scanning_total beiträgt. Die Aufgabe ist erledigt, wenn die Abrechnung der Metrik konsistent ist oder ihre beabsichtigte Semantik klar festgelegt und in der relevanten Dokumentation oder den Tests widergespiegelt ist.

Vom Indexierungsmodell aus dem Issue-Text verfasst.