## Step 1: Define the symbolic representation of the problem
The problem involves variables 'kiwis', 'bananas', and 'corn cobs' which can be represented symbolically as $x_0$, $x_1$, and $x_2$ respectively.

## Step 2: Translate the objective function into symbolic notation
The objective function to minimize is $8 \cdot kiwis + 4 \cdot bananas + 6 \cdot corn\ cobs$, which translates to $8x_0 + 4x_1 + 6x_2$.

## 3: List the constraints in symbolic notation
1. $4x_0 \leq 266$ (carbohydrates from kiwis)
2. $17x_0 \leq 381$ (cost of kiwis)
3. $22x_0 \leq 448$ (iron from kiwis)
4. $24x_1 \leq 266$ (carbohydrates from bananas)
5. $35x_1 \leq 381$ (cost of bananas)
6. $20x_1 \leq 448$ (iron from bananas)
7. $4x_2 \leq 266$ (carbohydrates from corn cobs)
8. $7x_2 \leq 381$ (cost of corn cobs)
9. $34x_2 \leq 448$ (iron from corn cobs)
10. $4x_0 + 4x_2 \geq 40$ (carbohydrates from kiwis and corn cobs)
11. $24x_1 + 4x_2 \geq 88$ (carbohydrates from bananas and corn cobs)
12. $4x_0 + 24x_1 + 4x_2 \geq 88$ (total carbohydrates)
13. $17x_0 + 7x_2 \geq 88$ (cost of kiwis and corn cobs)
14. $17x_0 + 35x_1 \geq 111$ (cost of kiwis and bananas)
15. $17x_0 + 35x_1 + 7x_2 \geq 111$ (total cost)
16. $20x_1 + 34x_2 \geq 130$ (iron from bananas and corn cobs)
17. $22x_0 + 20x_1 \geq 127$ (iron from kiwis and bananas)
18. $22x_0 + 20x_1 + 34x_2 \geq 127$ (total iron)
19. $6x_0 - 3x_2 \geq 0$ (relationship between kiwis and corn cobs)
20. $22x_0 + 20x_1 + 34x_2 \leq 360$ (total iron limit)

## 4: Define the symbolic variables and constraints for output
```json
{
    'sym_variables': [('x0', 'kiwis'), ('x1', 'bananas'), ('x2', 'corn cobs')],
    'objective_function': '8*x0 + 4*x1 + 6*x2',
    'constraints': [
        '4*x0 <= 266',
        '17*x0 <= 381',
        '22*x0 <= 448',
        '24*x1 <= 266',
        '35*x1 <= 381',
        '20*x1 <= 448',
        '4*x2 <= 266',
        '7*x2 <= 381',
        '34*x2 <= 448',
        '4*x0 + 4*x2 >= 40',
        '24*x1 + 4*x2 >= 88',
        '4*x0 + 24*x1 + 4*x2 >= 88',
        '17*x0 + 7*x2 >= 88',
        '17*x0 + 35*x1 >= 111',
        '17*x0 + 35*x1 + 7*x2 >= 111',
        '20*x1 + 34*x2 >= 130',
        '22*x0 + 20*x1 >= 127',
        '22*x0 + 20*x1 + 34*x2 >= 127',
        '6*x0 - 3*x2 >= 0',
        '22*x0 + 20*x1 + 34*x2 <= 360'
    ]
}
```

## 5: Write the Gurobi code
```python
import gurobi

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

# Define the variables
x0 = m.addVar(name="kiwis", lb=0)  # kiwis
x1 = m.addVar(name="bananas", lb=0)  # bananas
x2 = m.addVar(name="corn_cobs", lb=0)  # corn cobs

# Define the objective function
m.setObjective(8 * x0 + 4 * x1 + 6 * x2, gurobi.GRB.MINIMIZE)

# Add constraints
m.addConstr(4 * x0 <= 266)
m.addConstr(17 * x0 <= 381)
m.addConstr(22 * x0 <= 448)
m.addConstr(24 * x1 <= 266)
m.addConstr(35 * x1 <= 381)
m.addConstr(20 * x1 <= 448)
m.addConstr(4 * x2 <= 266)
m.addConstr(7 * x2 <= 381)
m.addConstr(34 * x2 <= 448)
m.addConstr(4 * x0 + 4 * x2 >= 40)
m.addConstr(24 * x1 + 4 * x2 >= 88)
m.addConstr(4 * x0 + 24 * x1 + 4 * x2 >= 88)
m.addConstr(17 * x0 + 7 * x2 >= 88)
m.addConstr(17 * x0 + 35 * x1 >= 111)
m.addConstr(17 * x0 + 35 * x1 + 7 * x2 >= 111)
m.addConstr(20 * x1 + 34 * x2 >= 130)
m.addConstr(22 * x0 + 20 * x1 >= 127)
m.addConstr(22 * x0 + 20 * x1 + 34 * x2 >= 127)
m.addConstr(6 * x0 - 3 * x2 >= 0)
m.addConstr(22 * x0 + 20 * x1 + 34 * x2 <= 360)

# Optimize the model
m.optimize()

# Print the solution
if m.status == gurobi.GRB.OPTIMAL:
    print("Objective: ", m.objval)
    print("Kiwis: ", x0.varValue)
    print("Bananas: ", x1.varValue)
    print("Corn Cobs: ", x2.varValue)
else:
    print("The model is infeasible")
```