The provided information describes a linear program that can be solved using Gurobi. The objective is to maximize the combined value of apple pies and cantaloupes, subject to constraints on their nutritional content (calcium, iron, protein) and a combined constraint involving the quantities of both.  The code below implements this linear program in Gurobi. Note the use of `GRB.CONTINUOUS` and `GRB.INTEGER` to enforce the non-integer and integer requirements for apple pies and cantaloupes, respectively. Redundant constraints (e.g., multiple upper bounds on the same quantity) are included as specified in the input.

```python
import gurobipy as gp
from gurobipy import GRB

try:
    # Create a new model
    m = gp.Model("nutrition_optimization")

    # Create variables
    apple_pies = m.addVar(vtype=GRB.CONTINUOUS, name="apple_pies")
    cantaloupes = m.addVar(vtype=GRB.INTEGER, name="cantaloupes")

    # Set objective function
    m.setObjective(1.42 * apple_pies + 8.78 * cantaloupes, GRB.MAXIMIZE)

    # Add constraints
    m.addConstr(3.47 * apple_pies + 0.49 * cantaloupes >= 19, "calcium_min")
    m.addConstr(4.7 * apple_pies + 3.14 * cantaloupes >= 23, "iron_min")
    m.addConstr(5.64 * apple_pies + 1.53 * cantaloupes >= 7, "protein_min")
    m.addConstr(3 * apple_pies - 9 * cantaloupes >= 0, "combined_constraint")

    m.addConstr(3.47 * apple_pies + 0.49 * cantaloupes <= 30, "calcium_max1")
    m.addConstr(3.47 * apple_pies + 0.49 * cantaloupes <= 30, "calcium_max2") # Redundant
    m.addConstr(4.7 * apple_pies + 3.14 * cantaloupes <= 28, "iron_max1")
    m.addConstr(4.7 * apple_pies + 3.14 * cantaloupes <= 28, "iron_max2") # Redundant
    m.addConstr(5.64 * apple_pies + 1.53 * cantaloupes <= 26, "protein_max1")
    m.addConstr(5.64 * apple_pies + 1.53 * cantaloupes <= 26, "protein_max2") # Redundant


    # Optimize model
    m.optimize()

    if m.status == GRB.OPTIMAL:
        print('Obj: %g' % m.objVal)
        print('apple_pies: %g' % apple_pies.x)
        print('cantaloupes: %g' % cantaloupes.x)
    elif m.status == GRB.INFEASIBLE:
        print('The model is infeasible.')
    else:
        print('Optimization ended with status %d' % m.status)


except gp.GurobiError as e:
    print('Error code ' + str(e.errno) + ': ' + str(e))

except AttributeError:
    print('Encountered an attribute error')
```
