Introduce SolverRelating type relation to the new solver

[compiler-errors]

Redux of #128744.

Splits out relate for the new solver so that implementors don't need to implement it themselves.

r? lcnr

[rustbot]

changes to the core type system

cc @compiler-errors, @lcnr

[compiler-errors]

but until we get rid of LUB/GLB this won't be fully possible

We could also, perhaps, get rid of CombineFields and just call infcx.at.eq when needing to equate in LUB. I could do that as a follow-up, especially because it has some overhead with perf since we'd need to clone obligationcauses.

[bors]

☔ The latest upstream changes (presumably #131226) made this pull request unmergeable. Please resolve the merge conflicts.

[compiler-errors]

Now that the other PR landed, I believe this is ready

[lcnr]

r=me after review

[lcnr]

@bors r+ rollup=never

[bors]

📌 Commit 8715bfb has been approved by lcnr

It is now in the queue for this repository.

[bors]

⌛ Testing commit 8715bfb with merge 8d94e06...

[bors]

☀️ Test successful - checks-actions
Approved by: lcnr
Pushing 8d94e06 to master...

[rust-timer]

Finished benchmarking commit (8d94e06): comparison URL.

Overall result: ❌ regressions - please read the text below

Our benchmarks found a performance regression caused by this PR.
This might be an actual regression, but it can also be just noise.

Next Steps:

• If the regression was expected or you think it can be justified,
  please write a comment with sufficient written justification, and add
  @rustbot label: +perf-regression-triaged to it, to mark the regression as triaged.
• If you think that you know of a way to resolve the regression, try to create
  a new PR with a fix for the regression.
• If you do not understand the regression or you think that it is just noise,
  you can ask the @rust-lang/wg-compiler-performance working group for help (members of this group
  were already notified of this PR).

@rustbot label: +perf-regression
cc @rust-lang/wg-compiler-performance

Instruction count

This is the most reliable metric that we have; it was used to determine the overall result at the top of this comment. However, even this metric can sometimes exhibit noise.

	mean	range	count
Regressions ❌ (primary)	0.2%	[0.2%, 0.2%]	1
Regressions ❌ (secondary)	0.5%	[0.2%, 2.7%]	9
Improvements ✅ (primary)	-	-	0
Improvements ✅ (secondary)	-	-	0
All ❌✅ (primary)	0.2%	[0.2%, 0.2%]	1

Max RSS (memory usage)

Results (primary -1.2%, secondary -0.1%)

This is a less reliable metric that may be of interest but was not used to determine the overall result at the top of this comment.

	mean	range	count
Regressions ❌ (primary)	0.4%	[0.4%, 0.4%]	1
Regressions ❌ (secondary)	2.0%	[1.9%, 2.1%]	3
Improvements ✅ (primary)	-1.9%	[-2.4%, -1.5%]	2
Improvements ✅ (secondary)	-2.2%	[-2.9%, -1.5%]	3
All ❌✅ (primary)	-1.2%	[-2.4%, 0.4%]	3

Cycles

Results (primary 0.7%, secondary -2.1%)

This is a less reliable metric that may be of interest but was not used to determine the overall result at the top of this comment.

	mean	range	count
Regressions ❌ (primary)	0.7%	[0.7%, 0.7%]	1
Regressions ❌ (secondary)	-	-	0
Improvements ✅ (primary)	-	-	0
Improvements ✅ (secondary)	-2.1%	[-2.1%, -2.1%]	1
All ❌✅ (primary)	0.7%	[0.7%, 0.7%]	1

Binary size

This benchmark run did not return any relevant results for this metric.

Bootstrap: 774.326s -> 779.131s (0.62%)
Artifact size: 329.54 MiB -> 332.04 MiB (0.76%)