The provided constraints have several redundancies. For example, the constraint "The total combined sourness index from ravioli plus pickles plus sashimi must be greater than or equal to 17" is repeated multiple times, and later a stricter constraint "The total combined sourness index from ravioli, pickles, and sashimi should be 28 at a minimum" is introduced, which supersedes the previous ones.  We will only include the strictest version of each constraint in the final model to avoid redundancy and potential conflicts.

```python
import gurobipy as gp

# Create a new model
m = gp.Model("sourness_optimization")

# Create variables
ravioli = m.addVar(lb=0, name="ravioli")
ham_sandwiches = m.addVar(lb=0, name="ham_sandwiches")
blueberry_pies = m.addVar(lb=0, name="blueberry_pies")
pickles = m.addVar(lb=0, name="pickles")
sashimi = m.addVar(lb=0, name="sashimi")

# Set objective function
m.setObjective(6.4 * ravioli + 3.16 * ham_sandwiches + 8.42 * blueberry_pies + 5.54 * pickles + 1.86 * sashimi, gp.GRB.MINIMIZE)

# Add constraints
sourness_index = {'ravioli': 20, 'ham_sandwiches': 19, 'blueberry_pies': 11, 'pickles': 13, 'sashimi': 12}
m.addConstr(sourness_index['blueberry_pies'] * blueberry_pies + sourness_index['pickles'] * pickles >= 16)
m.addConstr(sourness_index['ham_sandwiches'] * ham_sandwiches + sourness_index['pickles'] * pickles >= 20)
m.addConstr(sourness_index['ravioli'] * ravioli + sourness_index['pickles'] * pickles + sourness_index['sashimi'] * sashimi >= 28)
m.addConstr(sourness_index['ham_sandwiches'] * ham_sandwiches + sourness_index['blueberry_pies'] * blueberry_pies + sourness_index['pickles'] * pickles >= 28)
m.addConstr(sourness_index['ham_sandwiches'] * ham_sandwiches + sourness_index['blueberry_pies'] * blueberry_pies + sourness_index['sashimi'] * sashimi >= 28)
m.addConstr(sourness_index['ravioli'] * ravioli + sourness_index['ham_sandwiches'] * ham_sandwiches + sourness_index['pickles'] * pickles >= 28)
m.addConstr(sourness_index['ravioli'] * ravioli + sourness_index['blueberry_pies'] * blueberry_pies + sourness_index['pickles'] * pickles >= 28)
m.addConstr(sourness_index['blueberry_pies'] * blueberry_pies + sourness_index['pickles'] * pickles + sourness_index['sashimi'] * sashimi >= 28)
m.addConstr(sourness_index['ravioli'] * ravioli + sourness_index['ham_sandwiches'] * ham_sandwiches + sourness_index['blueberry_pies'] * blueberry_pies + sourness_index['pickles'] * pickles + sourness_index['sashimi'] * sashimi >= 28)
m.addConstr(-8 * ravioli + 7 * blueberry_pies >= 0)
m.addConstr(8 * ravioli - 4 * sashimi >= 0)
m.addConstr(sourness_index['pickles'] * pickles + sourness_index['sashimi'] * sashimi <= 136)


# Optimize model
m.optimize()

# Print results
if m.status == gp.GRB.OPTIMAL:
    print('Obj: %g' % m.objVal)
    for v in m.getVars():
        print('%s %g' % (v.varName, v.x))
elif m.status == gp.GRB.INFEASIBLE:
    print("The model is infeasible.")
else:
    print("Optimization ended with status %d" % m.status)

```
