Efficient channel state information (CSI) feedback is critical for 6G extremely large-scale multiple-input multiple-output (XL-MIMO) systems to mitigate channel interference. However, the massive antenna scale imposes a severe burden on feedback overhead. Meanwhile, existing quantized feedback methods face dual challenges of limited quantization precision and insufficient channel robustness when compressing high-dimensional channel features into discrete symbols. To reduce these gaps, guided by the deep joint source-channel coding (DJSCC) framework, we propose a vector quantized (VQ)-aided scheme for CSI feedback in XL-MIMO systems considering the near-field effect, named VQ-DJSCC-F. Firstly, taking advantage of the sparsity of near-field channels in the polar-delay domain, we extract energy-concentrated features to reduce dimensionality. Then, we simultaneously design the Transformer and CNN (convolutional neural network) architectures as the backbones to hierarchically extract CSI features, followed by VQ modules projecting features into a discrete latent space. The entropy loss regularization in synergy with an exponential moving average (EMA) update strategy is introduced to maximize quantization precision. Furthermore, we develop an attention mechanism-driven channel adaptation module to mitigate the impact of wireless channel fading on the transmission of index sequences. Simulation results demonstrate that the proposed scheme achieves superior CSI reconstruction accuracy with lower feedback overheads under varying channel conditions.