tftypes: Reduce AttributePath and Value type compute and memory usage

[bflad]

Closes #307

These optimizations were performed by adding benchmark tests against a set of 1000 "simple" objects and viewing the cpu/memory profiles. The original implementations were spending an immense amount of time dealing with memory allocations and garbage collection, so reducing memory allocations was the main target of these optimizations, which in turn, also reduced compute time.

The largest changes were accomplished by removing (Value).Diff() from the logic paths which were only testing equality. The new (Value).deepEqual() implementation started as a duplicate of that logic, removing all ValueDiff allocations. Then, the walking of Value needed a methodology to stop the walk immediately to prevent further allocations. The two changes in that regard were introducing a stopWalkError sentinel error type for which callback functions can signal that no remaining Value are necessary to traverse and updating the internal walk() implementation to include a new bool return to fully accomplish the intended behavior.

The other changes were smaller optimizations noticed from the profiling, such as avoiding the Go runtime immediately reallocating a larger slice with the AttributePath methods, avoiding memory allocations caused by (Type).Is() usage instead of using type switches, and directly referencing List, Map, Object, Set, and Tuple value storage instead of allocating an unneeded variable. This logic is heavily used both by this Go module and others, so even these minor optimizations have impact at scale.

benchstat differences between prior implementation and proposed changes:

goos: darwin
goarch: arm64
pkg: github.com/hashicorp/terraform-plugin-go/tftypes
                                             │   original   │              proposed               │
                                             │    sec/op    │   sec/op     vs base                │
Transform1000-10                               2886.1µ ± 0%   924.6µ ± 0%  -67.96% (p=0.000 n=10)
ValueApplyTerraform5AttributePathStep1000-10   3863.4µ ± 1%   628.9µ ± 0%  -83.72% (p=0.000 n=10)
geomean                                         3.339m        762.5µ       -77.16%

                                             │   original    │               proposed               │
                                             │     B/op      │     B/op      vs base                │
Transform1000-10                               3137.3Ki ± 0%   837.6Ki ± 0%  -73.30% (p=0.000 n=10)
ValueApplyTerraform5AttributePathStep1000-10   3887.1Ki ± 0%   389.3Ki ± 0%  -89.98% (p=0.000 n=10)
geomean                                         3.410Mi        571.0Ki       -83.65%

                                             │   original   │              proposed               │
                                             │  allocs/op   │  allocs/op   vs base                │
Transform1000-10                                61.07k ± 0%   14.02k ± 0%  -77.05% (p=0.000 n=10)
ValueApplyTerraform5AttributePathStep1000-10   123.07k ± 0%   17.64k ± 0%  -85.67% (p=0.000 n=10)
geomean                                         86.69k        15.72k       -81.86%

[bendbennett]

LGTM 🚀

[github-actions]

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