pyomo constraints for ruling out anti-parallel arcs

I am trying to model assignment problems with the additional constraint that the solution must not contain pairs of anti-parallel arcs, i.e. if in the solution x[i,j]+x[j,i]<=1 must hold for all Binary variables; I have copied an existing solution for assignment and try to add the constraints:

"""
Toy example for testing assignment without anti-parallel arcs
"""

from pyomo.environ import *
from pyomo.opt import *
opt = solvers.SolverFactory("glpk") 
# Change to "ipopt" for interior point solver

""" a larger instance that could be activated:
M = ['0', '1', '2','3','4','5','6']
W = ['0', '1', '2','3','4','5','6']

c = {('0','0'):0, ('0','1'):1, ('0','2'):0, ('0','3'):0, ('0','4'):0, ('0','5'):0, ('0','6'):0,
     ('1','0'):1, ('1','1'):0, ('1','2'):0, ('1','3'):0, ('1','4'):0, ('1','5'):0, ('1','6'):0,
     ('2','0'):0, ('2','1'):0, ('2','2'):0, ('2','3'):0, ('2','4'):0, ('2','5'):0, ('2','6'):0,
     ('3','0'):0, ('3','1'):0, ('3','2'):0, ('3','3'):0, ('3','4'):0, ('3','5'):0, ('3','6'):0,
     ('4','0'):0, ('4','1'):0, ('4','2'):0, ('4','3'):0, ('4','4'):0, ('4','5'):0, ('4','6'):0,
     ('5','0'):0, ('5','1'):0, ('5','2'):0, ('5','3'):0, ('5','4'):0, ('5','5'):0, ('5','6'):0,
     ('6','0'):0, ('6','1'):0, ('6','2'):0, ('6','3'):0, ('6','4'):0, ('6','5'):0, ('6','6'):0}    
"""

M = ['A', 'B', 'C']
W = ['D', 'E', 'F']

c = {('A','D'):1, ('A','E'):3, ('A','F'):3,
     ('B','D'):4, ('B','E'):3, ('B','F'):2,
     ('C','D'):5, ('C','E'):4, ('C','F'):2}
     

model = ConcreteModel()

model.x = Var(M, W, within=Binary)

model.z = Objective(expr = sum(c[i,j]*model.x[i,j]
                               for i in M for j in W), 
                    sense=maximize)

def all_m_assigned_rule (model, i):
    return sum(model.x[i,j] for j in W) == 1

model.m = Constraint(M, rule=all_m_assigned_rule)

def all_w_assigned_rule (model, j):
    return sum(model.x[i,j] for i in M) == 1

model.w = Constraint(W, rule=all_w_assigned_rule)


# additional model constraints that fail:
model.constraints = ConstraintList()
for i in I:
    for j in range(i):
        model.constraints.add((model.x[i,j]+model.x[j,i]) <= 1)

The essential reported error is

KeyError                                  Traceback (most recent call last)
Input In [12], in <cell line: 48>()
     48 for i in I:
     49     for j in range(i):
---> 50         model.constraints.add((model.x[i,j]+model.x[j,i]) <= 1)
     54 results = opt.solve(model)
     56 model.x.get_values()

Can anyone provide an explanation of the failure? A suggestion that fixes the problem would be great!