The following LP gives a solution that does not satisfy the expected envy-freeness constraints

def XEF_U_Allocation(N,p):
    
    # Define the linear program
    lp=gp.Model("""Envy-Free Utilitarian Allocation""")
    
    # Define the decision variables
    decision_vars = []
    k = len(N)
    n = N[0].shape[0]
    
#   m=N.shape[1]
    for l in range(k):
        for i in range(n):
            for a in range(n):
                decision_vars.append(lp.addVar(name=f'x_{l}{i}{a}'))
    
#   for l in range(k):
    decision_vars=np.array(decision_vars).reshape(k,n,n)
    
    # Define the objective function - Utilitarian objective 
    lp.setObjective(sum(N[l][i][a]*decision_vars[l][i][a]*p[l] for l in range (k) for i in range(n) for a in range(n)),GRB.MAXIMIZE)
    # *p[l]
    
    # Each agent receives exactly one object 
    for l in range(k):
        for i in range(n):
            lp.addConstr(sum(decision_vars[l][i][a] for a in range(n))==1)

    # Each object is allocated exactly one agent
    for l in range(k):
        for a in range(n):
            lp.addConstr(sum(decision_vars[l][i][a] for i in range(n))==1)

    
    # Expected envy-freeness constraints
    for l in range(k):
        for i in range(n):
            for j in [t for t in range(n) if t!=i]:
                lp.addConstr(sum((p[l] / (sum(p[t] for t in range(k) if np.array_equal(decision_vars[t][i],decision_vars[l][i]))))*decision_vars[t][i]*N[t][i] for t in range(k) if np.array_equal(decision_vars[t][i],decision_vars[l][i])) >= sum((p[l] / (sum(p[t] for t in range(k) if np.array_equal(decision_vars[t][i],decision_vars[l][i]))))*(decision_vars[t][j])*N[t][i] for t in range(k) if np.array_equal(decision_vars[t][i],decision_vars[l][i])))
                                                                                                                 
     lp.update()
    lp.optimize()
    lp.printQuality()
    solution=np.zeros((k,n,n))
    
    if lp.SolCount == 0:
        return "For the given instance, there is no expected envy-free allocation."
    else:
        for l in range(k):
            for i in range(n):
                for a in range(n):
                    solution[l][i][a]=decision_vars[l][i][a].X
    
        XEF_U_Allocation = solution 
        obj_exante = lp.getObjective()
        print(obj_exante.getValue())
        A = [XEF_U_Allocation, obj_exante.getValue()]
    
        return A