To solve the optimization problem described, we first need to translate the natural language description into a symbolic representation. This involves defining variables, an objective function, and constraints using algebraic notation.

### Symbolic Representation

Let's denote:
- $x_1$ as the number of apple pies,
- $x_2$ as the number of cantaloupes.

The objective function is to maximize: $1.42x_1 + 8.78x_2$

Given the constraints:
1. Calcium from apple pies and cantaloupes must be at least 19 milligrams: $3.47x_1 + 0.49x_2 \geq 19$
2. Iron from apple pies and cantaloupes must be at least 23 milligrams: $4.7x_1 + 3.14x_2 \geq 23$
3. Total protein from apple pies and cantaloupes must be at least 7 grams: $5.64x_1 + 1.53x_2 \geq 7$
4. Constraint on the combination of apple pies and cantaloupes: $3x_1 - 9x_2 \geq 0$
5. Calcium from apple pies and cantaloupes must not exceed 30 milligrams (note: this constraint seems to be mistakenly duplicated, but we'll consider it as part of the problem for completeness): $3.47x_1 + 0.49x_2 \leq 30$
6. Iron from apple pies and cantaloupes must not exceed 28 milligrams (again, noting the duplication, but proceeding with the inclusion of both upper bounds for completeness): $4.7x_1 + 3.14x_2 \leq 28$
7. Total protein from apple pies and cantaloupes must not exceed 26 grams: $5.64x_1 + 1.53x_2 \leq 26$

### Gurobi Code

To implement this problem in Gurobi, we will use Python as the interface.

```python
from gurobipy import *

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

# Define variables (apple pies can be non-integer, cantaloupes must be integer)
x1 = m.addVar(lb=0, ub=GRB.INFINITY, vtype=GRB.CONTINUOUS, name="apple_pies")
x2 = m.addVar(lb=0, ub=GRB.INFINITY, vtype=GRB.INTEGER, name="cantaloupes")

# Objective function: Maximize 1.42*x1 + 8.78*x2
m.setObjective(1.42*x1 + 8.78*x2, GRB.MAXIMIZE)

# Constraints
m.addConstr(3.47*x1 + 0.49*x2 >= 19, name="calcium_min")
m.addConstr(4.7*x1 + 3.14*x2 >= 23, name="iron_min")
m.addConstr(5.64*x1 + 1.53*x2 >= 7, name="protein_min")
m.addConstr(3*x1 - 9*x2 >= 0, name="combination_constraint")
m.addConstr(3.47*x1 + 0.49*x2 <= 30, name="calcium_max")
m.addConstr(4.7*x1 + 3.14*x2 <= 28, name="iron_max")
m.addConstr(5.64*x1 + 1.53*x2 <= 26, name="protein_max")

# Optimize model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Apple Pies: {x1.x}")
    print(f"Cantaloupes: {x2.x}")
    print(f"Objective Function Value: {m.objVal}")
else:
    print("No optimal solution found")
```