Perf: Add global caching and other optimizations

[oittaa]

• Implemented thread-safe GlobalCache (LRU) for expensive operations.
• Optimized GetCallInfo with per-package AST node caching.
• Cached GoVersion using sync.Once. -> Surprisingly profiling showed this to be a huge bottleneck on large code bases.
• Implemented entropy / secrets pre-filtering.
• Refactored #nosec directive parsing to avoid allocations.
• Migrated multiple rules (G101, G111, G114, G306) to use centralized caching.
• Verified 8.6x speedup on CockroachDB SQL package (37s -> 4.3s) and ~600MB memory reduction.

Performance Impact (Benchmarked on CockroachDB)

Metric	Baseline	Optimized	Delta
Execution Time (SQL Pkg)	37.0s	4.29s	8.6x Speedup
Full Root Scan Time	47.0s	12.3s	3.8x Speedup
Peak Memory	2.0 GB	1.4 GB	~600 MB Reduction

[codecov-commenter]

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Codecov Report

❌ Patch coverage is 90.96045% with 16 lines in your changes missing coverage. Please review.
✅ Project coverage is 69.19%. Comparing base (1216c9b) to head (9ff395d).
⚠️ Report is 165 commits behind head on master.

Files with missing lines	Patch %	Lines
rules/hardcoded_credentials.go	80.00%	6 Missing and 1 partial ⚠️
helpers.go	72.72%	4 Missing and 2 partials ⚠️
analyzer.go	95.58%	2 Missing and 1 partial ⚠️
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Additional details and impacted files

@@            Coverage Diff             @@
##           master    #1467      +/-   ##
==========================================
+ Coverage   68.49%   69.19%   +0.69%     
==========================================
  Files          75       83       +8     
  Lines        4384     6516    +2132     
==========================================
+ Hits         3003     4509    +1506     
- Misses       1233     1785     +552     
- Partials      148      222      +74     

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[oittaa]

Now also fixes #1240

[oittaa]

I also tried and abandoned some other optimization methods that didn't produce results. Maybe someone in the future is more successful in trying these.

1. Pipeline architecture by separating file reading+load and AST+SSA to their own goroutines. -> It used a bit more memory but runtime didn't improve at all.
2. Tried batch processing packages, but it just ate a whole lot more memory (multiples) and took more time.
3. Caching loaded packages in hope that it would increase performance. There are many common packages that are often included, but at least on my computer the runtime didn't change at all even with very high cache hit ratio. Memory usage just rose significantly (~50%).

[oittaa]

Here's another data point. Kubernetes scan time:

Version	Time
v2.22.11	4min 8.95s
Master	44.85s
This	14.75s

[oittaa]

Would you prefer if I split this into smaller changes? I just made this branch to investigate where the performance bottlenecks were and which ones were reasonably easy to tackle without going completely crazy. In the end this combination of changes seemed to offer a really nice balance of performance boosts without resorting to anything too weird.