When calling Gurobi by pyomo, if the primal is infeasible, how to find a random dual extreme points and extreme ray without coding the dual?

I am using pyomo to call gurobi to solve a linear programming model P. Let D be the dual problem of P. When P is infeasible, how can we get a random (not fixed) extreme point and extreme ray of D without coding D in pyomo?

My code is below. For simplicity, I used a tiny example with only two nonnegative variables (x1 and x2). The objective is to minimize 30x1 - 20x2, subject to -6x1 + 4x2 >= 12 and 5x1 - 5x2 >= 5.

Primal P is infeasible. By geometry, we know that the dual extreme points are (0, 6) and (0, 4). Dual extreme rays are (1, 0.8) and (1, 1.2). However, from the code below, I always get dual extreme point (0, 4) and dual extreme ray (-1, -0.8). I cannot get dual extreme point (0, 6) and dual extreme ray (1, 1.2). It seems that we can only get one fixed dual extreme point and one fixed dual extreme ray. The following is the code.

    from pyomo.environ import *
    import pyomo.environ as pe
    import pyomo.opt
    import gurobipy as gp
    # Define model
    model = ConcreteModel()
    model.x = Var([1, 2], within=NonNegativeReals)

    # Objective function
    model.obj = Objective(expr=30*model.x[1] - 20 * model.x[2], sense=minimize)

    # Infeasible constraints
    model.a_rule = Constraint(expr=-6 * model.x[1] + 4 * model.x[2] >= 12)
    model.b_rule = Constraint(expr= 5 * model.x[1] - 5 * model.x[2] >= 5)

    # Solve with Gurobi
    solver = SolverFactory('gurobi')
    solver.options['Method'] = 1  # Use dual simplex
    solver.options['InfUnbdInfo'] = 1  # Ensure infeasibility certificate is available
    results = solver.solve(model, tee=True, load_solutions=False)  # Do NOT load solutions

    # Find dual extreme points and extreme rays when the model is infeasible
    if results.solver.termination_condition == TerminationCondition.infeasible:
         lp_filename = "pyomo_model.lp"
         gurobi_model = gp.read(lp_filename)
         gurobi_model.setParam("Method", 1)  # Ensure Dual Simplex is used
         gurobi_model.setParam("InfUnbdInfo", 1)  # Enable infeasibility certificate
         gurobi_model.setParam("DualReductions", 0)  # Ensure extreme ray is available
         gurobi_model.setParam("PreSolve", 0)   # Disable preprocessing
         gurobi_model.optimize()

     # Find dual extreme points
     print ('Dual extreme points:')
     dual_values = gurobi_model.getAttr("Pi", gurobi_model.getConstrs())
     for constr, dual_value in zip(gurobi_model.getConstrs(), dual_values):
        print (constr, dual_value)
     
     # Find dual extreme rays
     print ('Dual extreme rays:')
     for constr in gurobi_model.getConstrs():
          farkas_dual = constr.getAttr("FarkasDual")
          print (constr, farkas_dual)