Here's the Gurobi code to solve the optimization problem. We've taken into account the resource constraints (resilience index and planting space), the objective function, and the integer constraint on the number of vines.  We've also consolidated redundant constraints (e.g., the multiple ways the combined resilience index and planting space constraints were expressed).

```python
from gurobipy import Model, GRB

# Create a new model
m = Model("Vine Optimization")

# Create variables
zucchini = m.addVar(vtype=GRB.INTEGER, name="zucchini")
cucumber = m.addVar(vtype=GRB.INTEGER, name="cucumber")

# Set objective function
m.setObjective(2.44 * zucchini + 6.82 * cucumber, GRB.MINIMIZE)

# Add constraints
m.addConstr(8 * zucchini + 15 * cucumber >= 66, "Resilience_min")
m.addConstr(8 * zucchini + 15 * cucumber <= 159, "Resilience_max")  # Using the provided upper bound

m.addConstr(6 * zucchini + 1 * cucumber >= 66, "Planting_min") # Assuming planting space is in sq. in.
m.addConstr(6 * zucchini + 1 * cucumber <= 172, "Planting_max") # Using the provided upper bound


m.addConstr(8 * zucchini - 3 * cucumber >= 0, "Zucchini_Cucumber_Ratio")

# Optimize model
m.optimize()

# Print results
if m.status == GRB.OPTIMAL:
    print('Obj: %g' % m.objVal)
    print('Zucchini Vines:', zucchini.x)
    print('Cucumber Vines:', cucumber.x)
elif m.status == GRB.INFEASIBLE:
    print('The model is infeasible.')
else:
    print('Optimization ended with status %d' % m.status)

```
