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Functional parcellation of the cerebral cortex from resting-state functional magnetic resonance imaging (rs-fMRI) is a fundamental step for large-scale brain network analysis. Learning functional parcellation from rs-fMRI is essentially a dimensionality reduction of vertex-wise functional connectivity (FC), where previous studies replied linear dimensionality reduction techniques, failing to capture the nonlinearity nature of brain functional organization. In this work, we propose a self-supervised representation learning framework for whole-cortical, group-level parcellation to release the linear constraints in feature extraction. Specifically, we first decouple FC strength from spatial proximity via distance-based residualization, enabling the learning of functionally meaningful features beyond geodesic constraints. A variational autoencoder (VAE) is then employed to learn compact and uncertainty-aware FC representations, augmented with a neighborhood-based contrastive objective to explicitly promote local functional coherence. Spatial continuity is further encouraged through Laplacian positional encoding prior to clustering. Experiments demonstrate that the proposed method consistently achieves better performance than widely used cortical parcellations, achieving improvement in functional homogeneity, functional boundary alignment, clustering quality, and consistency with task-evoked activation patterns.