## Step 1: Define the symbolic representation of the problem
The problem involves two variables: 'corn cobs' and 'apple pies', which we can denote as $x_1$ and $x_2$ respectively. The objective function to maximize is $6x_1 + 4x_2$. The constraints are:
- $x_1 + 13x_2 \leq 122$ (grams of fiber)
- $13x_1 + 12x_2 \leq 77$ (milligrams of calcium)
- $x_1 + 13x_2 \geq 25$ (at least 25 grams of fiber)
- $13x_1 + 12x_2 \geq 32$ (at least 32 milligrams of calcium)
- $-5x_1 + 4x_2 \geq 0$
- $x_1 + 13x_2 \leq 92$ (no more than 92 grams of fiber, redundant with the first constraint)
- $13x_1 + 12x_2 \leq 41$ (no more than 41 milligrams of calcium, redundant with the second constraint)

## 2: Simplify and correct the constraints
Upon review, we notice that some constraints are redundant or can be simplified:
- The constraints $x_1 + 13x_2 \leq 122$ and $x_1 + 13x_2 \leq 92$ can be combined into $x_1 + 13x_2 \leq 92$ since 92 is less than 122.
- The constraints $13x_1 + 12x_2 \leq 77$ and $13x_1 + 12x_2 \leq 41$ can be combined into $13x_1 + 12x_2 \leq 41$ since 41 is less than 77.

## 3: Define the symbolic representation
The symbolic representation is:
```json
{
    'sym_variables': [('x1', 'corn cobs'), ('x2', 'apple pies')],
    'objective_function': '6*x1 + 4*x2',
    'constraints': [
        'x1 + 13*x2 <= 92',
        '13*x1 + 12*x2 <= 41',
        'x1 + 13*x2 >= 25',
        '13*x1 + 12*x2 >= 32',
        '-5*x1 + 4*x2 >= 0'
    ]
}
```

## 4: Implement the problem in Gurobi
Now, let's implement this problem in Gurobi using Python:
```python
import gurobi

# Create a new model
model = gurobi.Model()

# Define the variables
x1 = model.addVar(name='corn_cobs', lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY)
x2 = model.addVar(name='apple_pies', lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY)

# Define the objective function
model.setObjective(6 * x1 + 4 * x2, gurobi.GRB.MAXIMIZE)

# Define the constraints
model.addConstr(x1 + 13 * x2 <= 92, name='fiber_constraint')
model.addConstr(13 * x1 + 12 * x2 <= 41, name='calcium_constraint')
model.addConstr(x1 + 13 * x2 >= 25, name='min_fiber_constraint')
model.addConstr(13 * x1 + 12 * x2 >= 32, name='min_calcium_constraint')
model.addConstr(-5 * x1 + 4 * x2 >= 0, name='balance_constraint')

# Optimize the model
model.optimize()

# Print the solution
if model.status == gurobi.GRB.OPTIMAL:
    print('Objective: ', model.objVal)
    print('Corn cobs: ', x1.x)
    print('Apple pies: ', x2.x)
else:
    print('The model is infeasible')
```