For extremely large-scale multiple-input multiple-output （XL-MIMO） enabled 6G communication systems， the near-field effect poses performance limitations for traditional channel estimation methods based on far-field plane wavefront models. Existing near-field schemes predominantly rely on the Fresnel approximation， which introduces modeling errors， and generally handle channel estimation and multi-user activation detection in separate steps， leading to error accumulation. To address these challenges， this paper proposes a joint near-field channel estimation and multi-user activation detection framework based on an accurate spherical wave channel model. Different from approximation-based near-field schemes， the proposed framework directly characterizes the near-field array manifold without relying on Fresnel approximation， thereby avoiding model mismatch. Furthermore， the proposed method jointly exploits the double sparsity of XL-MIMO channels in both the polar domain and the user domain. Specifically， the polar domain sparsity arises from the limited number of effective propagation paths described by angle and distance parameters， while the user domain sparsity comes from the fact that only a small subset of users is active during a given transmission interval. By leveraging these two types of sparsity simultaneously， the original high-dimensional coupled estimation problem can be transformed into a more structured sparse recovery problem. Moreover， an independent-vector variational Bayesian inference （VBI） technique is introduced to efficiently decouple the complicated joint estimation task into multiple user-wise sparse recovery subproblems. This strategy enables effective separation between the near-field array manifold and multi-user pilot signals， thereby substantially reducing computational complexity while preserving estimation accuracy. In addition， an off-grid parameter update strategy is incorporated to refine the predefined angular and distance grids. By iteratively updating the off-grid parameters， the proposed method alleviates grid mismatch and improves the accuracy of near-field channel reconstruction. Simulation results demonstrate that the proposed method outperforms existing methods in terms of channel estimation accuracy and user activity detection reliability. Under different signal-to-noise ratios， antenna numbers， snapshot lengths， and active user numbers， the proposed algorithm consistently obtains lower normalized mean square error and higher user activity detection success rate. These results verify the effectiveness， robustness， and superiority of the proposed framework. Therefore， the proposed method provides an efficient and accurate solution for near-field XL-MIMO channel acquisition and grant-free massive access in future 6G wireless communication systems.