Digital twins (DTs) can generate dense synthetic fingerprints for localization via ray tracing, yet the sim-to-real domain gap limits their direct use, particularly in cell-free millimeter-wave (mmWave) deployments where distributed geometries and blockage-induced sparsity widen the mismatch. We propose DT-conditioned (DTCond), a framework that fuses real and DT beam-domain fingerprints as complementary inputs through per-domain encoders, learnable per-access point (AP) attention, and calibrated uncertainty. We derive oracle and pipeline Cramér-Rao bounds (CRBs) for DT-aided fingerprint localization, proving that DT information strictly tightens the positioning bound whenever spatial structure is preserved, with a heteroscedastic refinement that tightens the bound by a further 9.1%. On a real 28 GHz cell-free testbed, the deployable two-stage pipeline, which reads the DT fingerprint at a coarse k-nearest neighbor (k-NN) estimate, achieves 0.284 m mean error against 0.313 m for real-only k-NN. With the DT fingerprint read at the true position (oracle input), DT conditioning achieves 0.186 m mean error, validated through a ten-method ablation against domain-adaptation (DA) and transfer-learning baselines, and a 48% improvement on unseen regions. Multi-user self-attention provides 31-41% per-user gain for emulated simultaneous users, and progressive calibration reduces the measurement budget by 50%. As a use case, localization drives network configuration in closed loop. Between beam sweeps, a DT updated from measured RSS and read at the tracked position estimate selects the 3 of 81 beam pairs that each AP trains. With 49 calibration anchors in the update, the mean beam-selection loss stays below 1 dB over 2 m of motion, whereas reusing the last sweep loses 2.7 dB after 1 m.

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