## Step 1: Define the symbolic representation of the problem
The problem involves two variables: 'apple pies' and 'cantaloupes', which we can denote as $x_1$ and $x_2$ respectively. The objective function to maximize is $1.42x_1 + 8.78x_2$. The constraints are as follows:
- $3.47x_1 + 0.49x_2 \geq 19$ (calcium constraint)
- $4.7x_1 + 3.14x_2 \geq 23$ (iron constraint)
- $5.64x_1 + 1.53x_2 \geq 7$ (protein constraint)
- $3x_1 - 9x_2 \geq 0$ (additional constraint)
- $3.47x_1 + 0.49x_2 \leq 30$ (upper bound calcium constraint)
- $4.7x_1 + 3.14x_2 \leq 28$ (upper bound iron constraint)
- $5.64x_1 + 1.53x_2 \leq 26$ (upper bound protein constraint)

## Step 2: Convert the problem into a Gurobi-compatible format
We need to define the variables, the objective function, and the constraints in a way that Gurobi can understand.

## 3: Write down the symbolic representation
The symbolic representation of the problem is:
```json
{
'sym_variables': [('x1', 'apple pies'), ('x2', 'cantaloupes')],
'objective_function': '1.42*x1 + 8.78*x2',
'constraints': [
    '3.47*x1 + 0.49*x2 >= 19',
    '4.7*x1 + 3.14*x2 >= 23',
    '5.64*x1 + 1.53*x2 >= 7',
    '3*x1 - 9*x2 >= 0',
    '3.47*x1 + 0.49*x2 <= 30',
    '4.7*x1 + 3.14*x2 <= 28',
    '5.64*x1 + 1.53*x2 <= 26'
]
}
```

## 4: Implement the problem in Gurobi
Now, let's implement this problem using Gurobi in Python.

```python
import gurobi

def solve_optimization_problem():
    # Create a new model
    model = gurobi.Model()

    # Define the variables
    x1 = model.addVar(name="apple_pies", lb=0)  # You can use a non-integer number of apple pies
    x2 = model.addVar(name="cantaloupes", lb=0, integrality=1)  # You're limited to a nonfractional number of cantaloupes

    # Define the objective function
    model.setObjective(1.42 * x1 + 8.78 * x2, gurobi.GRB.MAXIMIZE)

    # Define the constraints
    model.addConstr(3.47 * x1 + 0.49 * x2 >= 19)  
    model.addConstr(4.7 * x1 + 3.14 * x2 >= 23)  
    model.addConstr(5.64 * x1 + 1.53 * x2 >= 7)  
    model.addConstr(3 * x1 - 9 * x2 >= 0)  
    model.addConstr(3.47 * x1 + 0.49 * x2 <= 30)  
    model.addConstr(4.7 * x1 + 3.14 * x2 <= 28)  
    model.addConstr(5.64 * x1 + 1.53 * x2 <= 26)  

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Objective: ", model.objval)
        print("Apple pies: ", x1.varValue)
        print("Cantaloupes: ", x2.varValue)
    else:
        print("The model is infeasible")

solve_optimization_problem()
```