feat: Phase 3 - Pydantic models and predefined scenarios

Add Pydantic models for API validation and 5 project scenarios

Pydantic Models:
- models/config.py: SimulationConfigModel with full validation
  - ServerConfig for each server in the network
  - Probability conservation validation
  - Conversion to internal SimulationConfig

- models/results.py: Complete results models for API responses
  - QueueStatisticsModel per queue
  - TimeSeriesDataModel for evolution tracking
  - HistogramDataModel for processing time distribution
  - SimulationResultsModel with all metrics

Predefined Scenarios:
- scenarios.py: 5 scenarios from project requirements
  - Scenario 1: 1 fast server (120ms) - instability test
  - Scenario 2: 1 fast + 1 slow server (120ms/240ms)
  - Scenario 3: 3 slow servers (240ms each)
  - Scenario 4: 1 fast + 1 medium (120ms/190ms) - compare with scenario 3
  - Scenario 5: Parameter sensitivity (vary λ and p)

- Theoretical utilization calculations for each scenario
- Scenario registry for easy access
- list_scenarios() function for API

Testing:
- test_all_scenarios.py: Comprehensive test of all scenarios
- Runs all 5 scenarios with variations
- Compares theoretical vs simulation results
- Summary table for performance comparison

Results Analysis:
- All scenarios execute successfully
- Stable systems show ρ < 1 as expected
- Some scenarios show slight instability (ρ ≈ 1.0) due to high load
- Parameter sensitivity variations demonstrate impact of λ and p

Phase 3 Complete ✓
Next: Phase 4 - Analytical module (Jackson's theorem)