Here's the Gurobi code to solve the optimization problem:

```python
import gurobipy as gp

# Create a new model
m = gp.Model("food_optimization")

# Create variables
eggs = m.addVar(lb=0, name="eggs")
hot_dogs = m.addVar(lb=0, name="hot_dogs")

# Set objective function
m.setObjective(5.97 * eggs + 9.26 * hot_dogs, gp.GRB.MAXIMIZE)

# Add constraints
m.addConstr(7 * eggs + 6 * hot_dogs >= 12, "fiber_min")
m.addConstr(1 * eggs + 9 * hot_dogs >= 4, "sourness_min")
m.addConstr(-3 * eggs + 2 * hot_dogs >= 0, "constraint3")
m.addConstr(7 * eggs + 6 * hot_dogs <= 26, "fiber_max") # Combined fiber constraint
m.addConstr(1 * eggs + 9 * hot_dogs <= 21, "sourness_max") # Combined sourness constraint


# Optimize model
m.optimize()

# Print results
if m.status == gp.GRB.OPTIMAL:
    print(f"Optimal objective value: {m.objVal}")
    print(f"Number of eggs: {eggs.x}")
    print(f"Number of hot dogs: {hot_dogs.x}")
elif m.status == gp.GRB.INFEASIBLE:
    print("The model is infeasible.")
else:
    print(f"Optimization ended with status {m.status}")

```
