To solve this optimization problem using Gurobi, we first need to understand and possibly simplify or restate the given constraints and objective function.

Given:
- Variables: `corn_cobs` (x0) and `apple_pies` (x1)
- Objective Function: Maximize 6*x0 + 4*x1
- Constraints:
  1. Fiber from corn cobs and apple pies: x0*1 + x1*13 >= 25 and <= 92
  2. Calcium from corn cobs and apple pies: x0*13 + x1*12 >= 32 and <= 41
  3. Additional constraint: -5*x0 + 4*x1 >= 0

The constraints regarding fiber and calcium have both lower and upper bounds, but the upper bound for calcium is more restrictive than that for fiber when considering the coefficients of x0 and x1.

Here's how we can express this problem in Gurobi:

```python
from gurobipy import *

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

# Define variables
corn_cobs = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="corn_cobs")
apple_pies = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="apple_pies")

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

# Constraints
# 1. Fiber constraint
m.addConstr(corn_cobs + 13*apple_pies >= 25, name="fiber_lower_bound")
m.addConstr(corn_cobs + 13*apple_pies <= 92, name="fiber_upper_bound")

# 2. Calcium constraint
m.addConstr(13*corn_cobs + 12*apple_pies >= 32, name="calcium_lower_bound")
m.addConstr(13*corn_cobs + 12*apple_pies <= 41, name="calcium_upper_bound")

# 3. Additional constraint
m.addConstr(-5*corn_cobs + 4*apple_pies >= 0, name="additional_constraint")

# Optimize model
m.optimize()

# Print results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Corn cobs: {corn_cobs.x}")
    print(f"Apple pies: {apple_pies.x}")
    print(f"Objective function value: {m.objVal}")
else:
    print("No optimal solution found.")
```