Random generation policies

Version 0.27.0 adds European QMC pricing across Rust, Python, C and Go. Rust uses MakeMcEuropeanEngine::<LowDiscrepancy>; Python and Go expose QMCEuropeanEngine, and the C engine factory selects kind 3. All use Sobol/Jaeckel points transformed to standard normal variates. Supply positive fixed samples and either positive steps or steps per year. Statistical error estimates, absolute tolerances and max_samples are unavailable for QMC. Existing Monte Carlo constructors continue to use PseudoRandom.

The QuantLib testQmcEngines grid covers 108 European calls/puts with one step and 4095 samples. Its acceptance is abs(QMC - analytic) / spot <= 0.01. The first Sobol uniform point is 0.5 in every dimension, so the first Gaussian point is zero. Sobol skips the all-zero point; copied generators preserve their current positions. QMC rejects sample counts above the 2**32 - 1 Sobol period.

Python and Go also expose scalar and sequence Poisson generators. Rates are per-generator, finite and positive; Python defaults to one. Seed zero selects a random MT19937 seed. Copying preserves state and subsequent draws are independent. Sequence results are copies; the last successful sample survives a failed draw.

Rust supplies InverseCumulativeRng, FallibleInverseCumulativeRsg, GenericLowDiscrepancy, LowDiscrepancy and PoissonPseudoRandom. The explicit inverse-transform constructors replace QuantLib's mutable global icInstance. Poisson sequences return QlResult and intentionally do not implement the infallible McRngTraits contract: an extreme quantile can be numerically unresolved. Failed transformations consume the attempted uniform draw and report the error. The normal fallible transform rejects endpoints; Poisson accepts zero and rejects one. Existing infallible Gaussian APIs remain unchanged. Rates whose exponential seed underflows are rejected.

Sources: QuantLib/ql/math/randomnumbers/rngtraits.hpp, QuantLib/test-suite/rngtraits.cpp, and QuantLib/test-suite/europeanoption.cpp:testQmcEngines.