A central challenge in affective computing is determining appropriate empathy levels for different interaction contexts. Prior work has characterized two poles: task-focused interactions, where empathy demand is near zero, and emotional disclosure, where empathy demand is high. This paper identifies a distinct intermediate type, decision support under stress, in which a sender faces a consequential choice while experiencing emotional difficulty. We hypothesize that this type elicits an asymmetric empathy profile: empathy comparable to emotional disclosure but instrumentality comparable to task-focused exchange. We test five hypotheses using 28,239 post-reply dyads from three Reddit advice communities, classified into three interaction types and scored for empathy depth, empathy form, and instrumental proportion using LLM-based annotation with pattern-based robustness checks. Results confirm the predicted asymmetric profile: decision-support-under-stress replies show significantly higher empathy than task-focused replies (M = 0.47 vs. 0.24, p < 0.001) while maintaining high instrumentality (0.83 vs. 0.77 for emotional disclosure, p < 0.001). Behavioral empathy dominates (36.6%), and community-validated response quality is negatively associated with empathic expression (r = -0.075, p < 0.001). Community norms modulate baselines substantially but preserve the structural ordering. These findings establish a human empathy baseline for this interaction type and have direct implications for calibrating empathic expression in affective AI systems.