Revert OTel top-level spans identification changes

[liustanley]

What does this PR do?

Reverts the changes from the following PRs:

• Add warn log for OTel top-level spans change #24156
• Update compute_stats_by_span_kind documentation #23945
• Update default logic for OTel top-level spans identification #22163

Motivation

The OTel top-level spans identification changes have been delayed until we have a new policy for releasing breaking changes for OTel customers.

Additional Notes

Possible Drawbacks / Trade-offs

Describe how to test/QA your changes

[dustmop]

LGTM for agent-shared-components owned files

[brett0000FF]

Just a small suggestion from Docs team to tweak the tense.

[brett0000FF]

Suggested change

	## If you are sending OTel traces and want stats on non-top-level spans, this flag will need to be enabled.
	## If you are sending OTel traces and want stats on non-top-level spans, this flag needs to be enabled.

[pr-commenter]

Test changes on VM

Use this command from test-infra-definitions to manually test this PR changes on a VM:

inv create-vm --pipeline-id=31121697 --os-family=ubuntu

[pr-commenter]

Regression Detector

Regression Detector Results

Run ID: 15a97357-f9b8-44c7-a33f-4d192039e638
Baseline: 0d3dcb9
Comparison: 3509f03

Performance changes are noted in the perf column of each table:

• ✅ = significantly better comparison variant performance
• ❌ = significantly worse comparison variant performance
• ➖ = no significant change in performance

No significant changes in experiment optimization goals

Confidence level: 90.00%
Effect size tolerance: |Δ mean %| ≥ 5.00%

There were no significant changes in experiment optimization goals at this confidence level and effect size tolerance.

Experiments ignored for regressions

Regressions in experiments with settings containing erratic: true are ignored.

perf	experiment	goal	Δ mean %	Δ mean % CI
➖	file_to_blackhole	% cpu utilization	+0.04	[-6.04, +6.13]

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI
➖	basic_py_check	% cpu utilization	+1.08	[-1.35, +3.51]
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	+1.05	[-1.86, +3.96]
➖	process_agent_standard_check	memory utilization	+0.11	[+0.05, +0.17]
➖	file_to_blackhole	% cpu utilization	+0.04	[-6.04, +6.13]
➖	tcp_dd_logs_filter_exclude	ingress throughput	+0.01	[-0.02, +0.04]
➖	trace_agent_msgpack	ingress throughput	-0.00	[-0.00, +0.00]
➖	uds_dogstatsd_to_api	ingress throughput	-0.00	[-0.20, +0.20]
➖	trace_agent_json	ingress throughput	-0.00	[-0.04, +0.04]
➖	file_tree	memory utilization	-0.06	[-0.15, +0.03]
➖	process_agent_real_time_mode	memory utilization	-0.14	[-0.19, -0.10]
➖	otel_to_otel_logs	ingress throughput	-0.42	[-0.84, -0.00]
➖	process_agent_standard_check_with_stats	memory utilization	-0.47	[-0.53, -0.42]
➖	idle	memory utilization	-0.58	[-0.62, -0.54]
➖	tcp_syslog_to_blackhole	ingress throughput	-0.60	[-0.70, -0.51]
➖	pycheck_1000_100byte_tags	% cpu utilization	-0.98	[-5.86, +3.89]

Explanation

A regression test is an A/B test of target performance in a repeatable rig, where "performance" is measured as "comparison variant minus baseline variant" for an optimization goal (e.g., ingress throughput). Due to intrinsic variability in measuring that goal, we can only estimate its mean value for each experiment; we report uncertainty in that value as a 90.00% confidence interval denoted "Δ mean % CI".

For each experiment, we decide whether a change in performance is a "regression" -- a change worth investigating further -- if all of the following criteria are true:

1. Its estimated |Δ mean %| ≥ 5.00%, indicating the change is big enough to merit a closer look.

2. Its 90.00% confidence interval "Δ mean % CI" does not contain zero, indicating that if our statistical model is accurate, there is at least a 90.00% chance there is a difference in performance between baseline and comparison variants.

3. Its configuration does not mark it "erratic".

[liustanley]

Closing in favor of #24232