To solve this optimization problem, we first need to translate the given natural language description into a mathematical formulation that can be solved using Gurobi. The problem involves maximizing an objective function subject to several constraints.

The variables in this problem are:
- `x0`: The number of apple pies.
- `x1`: The quantity of cantaloupes.

The objective function to maximize is: `1.42 * x0 + 8.78 * x1`.

The constraints based on the resources/attributes are:
1. Calcium from apple pies and cantaloupes must be at least 19 milligrams but no more than 30 milligrams.
   - Lower bound: `3.47*x0 + 0.49*x1 >= 19`
   - Upper bound: `3.47*x0 + 0.49*x1 <= 30`

2. Iron from apple pies and cantaloupes must be at least 23 milligrams but no more than 28 milligrams.
   - Lower bound: `4.7*x0 + 3.14*x1 >= 23`
   - Upper bound: `4.7*x0 + 3.14*x1 <= 28`

3. Total grams of protein from apple pies and cantaloupes must be at least 7 grams but no more than 26 grams.
   - Lower bound: `5.64*x0 + 1.53*x1 >= 7`
   - Upper bound: `5.64*x0 + 1.53*x1 <= 26`

4. The constraint involving the number of apple pies and cantaloupes: `3*x0 - 9*x1 >= 0`.

Since we are allowed to use a non-integer number of apple pies but limited to a nonfractional (integer) number of cantaloupes, we will define `x0` as a continuous variable and `x1` as an integer variable.

Now, let's write the Gurobi code to solve this problem:

```python
from gurobipy import *

# Create a model
m = Model("Optimization_Problem")

# Define variables
x0 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="apple_pies")
x1 = m.addVar(lb=0, vtype=GRB.INTEGER, name="cantaloupes")

# Objective function: Maximize 1.42*x0 + 8.78*x1
m.setObjective(1.42 * x0 + 8.78 * x1, GRB.MAXIMIZE)

# Constraints
m.addConstr(3.47*x0 + 0.49*x1 >= 19, "calcium_lower_bound")
m.addConstr(3.47*x0 + 0.49*x1 <= 30, "calcium_upper_bound")

m.addConstr(4.7*x0 + 3.14*x1 >= 23, "iron_lower_bound")
m.addConstr(4.7*x0 + 3.14*x1 <= 28, "iron_upper_bound")

m.addConstr(5.64*x0 + 1.53*x1 >= 7, "protein_lower_bound")
m.addConstr(5.64*x0 + 1.53*x1 <= 26, "protein_upper_bound")

m.addConstr(3*x0 - 9*x1 >= 0, "pies_cantaloupes_constraint")

# Optimize model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Apple Pies: {x0.x}")
    print(f"Cantaloupes: {x1.x}")
    print(f"Objective Function Value: {m.objVal}")
else:
    print("No optimal solution found.")
```