chore(profiling): reduce log spam in native code

[sanchda]

This PR

1. standardizes all native output to cerr
2. updates some language to be in-line with the actual code
3. within each function, puts a function-wide block so that logs can only be emitted one time

The inspiration from this is recent escalations and incidents where user code hits a library error at an unprecedented rate; the native code has no limiting/bucketing mechanism at the current time, so this is a desperate first attempt at preventing end-user logs from blowing up.

Checklist

• PR author has checked that all the criteria below are met
• The PR description includes an overview of the change
• The PR description articulates the motivation for the change
• The change includes tests OR the PR description describes a testing strategy
• The PR description notes risks associated with the change, if any
• Newly-added code is easy to change
• The change follows the library release note guidelines
• The change includes or references documentation updates if necessary
• Backport labels are set (if applicable)

Reviewer Checklist

• Reviewer has checked that all the criteria below are met
• Title is accurate
• All changes are related to the pull request's stated goal
• Avoids breaking API changes
• Testing strategy adequately addresses listed risks
• Newly-added code is easy to change
• Release note makes sense to a user of the library
• If necessary, author has acknowledged and discussed the performance implications of this PR as reported in the benchmarks PR comment
• Backport labels are set in a manner that is consistent with the release branch maintenance policy

[github-actions]

CODEOWNERS have been resolved as:

ddtrace/internal/datadog/profiling/dd_wrapper/include/libdatadog_helpers.hpp  @DataDog/profiling-python
ddtrace/internal/datadog/profiling/dd_wrapper/src/crashtracker.cpp      @DataDog/profiling-python
ddtrace/internal/datadog/profiling/dd_wrapper/src/ddup_interface.cpp    @DataDog/profiling-python
ddtrace/internal/datadog/profiling/dd_wrapper/src/profile.cpp           @DataDog/profiling-python
ddtrace/internal/datadog/profiling/dd_wrapper/src/sample.cpp            @DataDog/profiling-python
ddtrace/internal/datadog/profiling/dd_wrapper/src/synchronized_sample_pool.cpp  @DataDog/profiling-python
ddtrace/internal/datadog/profiling/dd_wrapper/src/uploader.cpp          @DataDog/profiling-python

[github-actions]

Bootstrap import analysis

Comparison of import times between this PR and base.

Summary

The average import time from this PR is: 234 ± 4 ms.

The average import time from base is: 234 ± 3 ms.

The import time difference between this PR and base is: -0.3 ± 0.2 ms.

The difference is not statistically significant (z = -1.79).

Import time breakdown

The following import paths have shrunk:

ddtrace.auto 1.843 ms (0.79%)

ddtrace.bootstrap.sitecustomize 1.177 ms (0.50%)

ddtrace.bootstrap.preload 1.177 ms (0.50%)

ddtrace.internal.products 1.177 ms (0.50%)

ddtrace.internal.remoteconfig.client 0.578 ms (0.25%)

ddtrace 0.666 ms (0.28%)