To solve the given optimization problem, we first need to translate the natural language description into a symbolic representation. This involves defining variables, an objective function, and constraints based on the problem statement.

Let's define:
- `x0` as the number of corn cobs,
- `x1` as the number of apple pies.

The objective function is to maximize `6*x0 + 4*x1`.

Given constraints:
1. Fiber from corn cobs: `1*x0`
2. Calcium in corn cobs: `13*x0`
3. Fiber from apple pies: `13*x1`
4. Calcium in apple pies: `12*x1`
5. Minimum fiber requirement: `x0 + 13*x1 >= 25`
6. Minimum calcium requirement: `13*x0 + 12*x1 >= 32`
7. Additional constraint: `-5*x0 + 4*x1 >= 0`
8. Maximum fiber limit: `x0 + 13*x1 <= 92`
9. Maximum calcium limit: `13*x0 + 12*x1 <= 41`

Symbolic representation:
```json
{
    'sym_variables': [('x0', 'corn cobs'), ('x1', 'apple pies')],
    'objective_function': '6*x0 + 4*x1',
    'constraints': [
        'x0 + 13*x1 >= 25',
        '13*x0 + 12*x1 >= 32',
        '-5*x0 + 4*x1 >= 0',
        'x0 + 13*x1 <= 92',
        '13*x0 + 12*x1 <= 41'
    ]
}
```

Now, let's implement this problem using Gurobi in Python:

```python
from gurobipy import *

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

# Define variables
x0 = m.addVar(lb=0, name="corn_cobs")  # Corn cobs can be any non-negative value (float)
x1 = m.addVar(lb=0, name="apple_pies")  # Apple pies can also be any non-negative float

# Objective function: Maximize
m.setObjective(6*x0 + 4*x1, GRB.MAXIMIZE)

# Constraints
m.addConstr(x0 + 13*x1 >= 25, "Minimum_Fiber")
m.addConstr(13*x0 + 12*x1 >= 32, "Minimum_Calcium")
m.addConstr(-5*x0 + 4*x1 >= 0, "Additional_Constraint")
m.addConstr(x0 + 13*x1 <= 92, "Maximum_Fiber")
m.addConstr(13*x0 + 12*x1 <= 41, "Maximum_Calcium")

# Optimize model
m.optimize()

if m.status == GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Corn cobs: {x0.x}, Apple pies: {x1.x}")
else:
    print("No optimal solution found.")
```