## Step 1: Define the symbolic representation of the variables
The variables are 'fruit salads', 'hamburgers', 'kiwis', 'chicken thighs' which can be represented symbolically as $x_0, x_1, x_2, x_3$ respectively.

## Step 2: Convert the objective function into a symbolic representation
The objective function to minimize is $3x_0 + 3x_1 + 1x_2 + 9x_3$.

## Step 3: List all the constraints in symbolic notation
1. $5.59x_0 + 1.36x_1 + 7.89x_2 + 1.43x_3 \leq 110$ (carbohydrates constraint)
2. $4.03x_0 + 6.73x_1 + 5.21x_2 + 7.13x_3 \leq 122$ (iron constraint)
3. $5.59x_0 + 7.89x_2 \geq 10$ (carbohydrates from fruit salads and kiwis)
4. $1.36x_1 + 1.43x_3 \geq 18$ (carbohydrates from hamburgers and chicken thighs)
5. $7.89x_2 + 1.43x_3 \geq 10$ (carbohydrates from kiwis and chicken thighs)
6. $5.59x_0 + 7.89x_2 + 1.43x_3 \geq 27$ (carbohydrates from fruit salads, kiwis, and chicken thighs)
7. $5.59x_0 + 1.36x_1 + 7.89x_2 + 1.43x_3 \geq 27$ (carbohydrates from all sources)
8. $6.73x_1 + 5.21x_2 \geq 24$ (iron from hamburgers and kiwis)
9. $4.03x_0 + 6.73x_1 \geq 18$ (iron from fruit salads and hamburgers)
10. $6.73x_1 + 7.13x_3 \geq 12$ (iron from hamburgers and chicken thighs)
11. $4.03x_0 + 7.13x_3 \geq 12$ (iron from fruit salads and chicken thighs)
12. $4.03x_0 + 6.73x_1 + 5.21x_2 + 7.13x_3 \geq 12$ (iron from all sources)
13. $9x_0 - 8x_2 \geq 0$ (fruit salads and kiwis constraint)
14. $-4x_1 + 10x_2 \geq 0$ (hamburgers and kiwis constraint)
15. $5.59x_0 + 1.43x_3 \leq 96$ (carbohydrates from fruit salads and chicken thighs)
16. $1.36x_1 + 7.89x_2 \leq 39$ (carbohydrates from hamburgers and kiwis)
17. $1.36x_1 + 1.43x_3 \leq 98$ (carbohydrates from hamburgers and chicken thighs)
18. $5.59x_0 + 1.36x_1 + 1.43x_3 \leq 67$ (carbohydrates from fruit salads, hamburgers, and chicken thighs)
19. $4.03x_0 + 7.13x_3 \leq 41$ (iron from fruit salads and chicken thighs)
20. $6.73x_1 + 7.13x_3 \leq 122$ (iron from hamburgers and chicken thighs)
21. $6.73x_1 + 5.21x_2 + 7.13x_3 \leq 75$ (iron from hamburgers, kiwis, and chicken thighs)
22. $4.03x_0 + 6.73x_1 + 7.13x_3 \leq 99$ (iron from fruit salads, hamburgers, and chicken thighs)
23. $4.03x_0 + 5.21x_2 + 7.13x_3 \leq 87$ (iron from fruit salads, kiwis, and chicken thighs)

## Step 4: Provide the symbolic representation in JSON format
```json
{
    'sym_variables': [
        ('x0', 'fruit salads'),
        ('x1', 'hamburgers'),
        ('x2', 'kiwis'),
        ('x3', 'chicken thighs')
    ],
    'objective_function': '3*x0 + 3*x1 + 1*x2 + 9*x3',
    'constraints': [
        '5.59*x0 + 1.36*x1 + 7.89*x2 + 1.43*x3 <= 110',
        '4.03*x0 + 6.73*x1 + 5.21*x2 + 7.13*x3 <= 122',
        '5.59*x0 + 7.89*x2 >= 10',
        '1.36*x1 + 1.43*x3 >= 18',
        '7.89*x2 + 1.43*x3 >= 10',
        '5.59*x0 + 7.89*x2 + 1.43*x3 >= 27',
        '5.59*x0 + 1.36*x1 + 7.89*x2 + 1.43*x3 >= 27',
        '6.73*x1 + 5.21*x2 >= 24',
        '4.03*x0 + 6.73*x1 >= 18',
        '6.73*x1 + 7.13*x3 >= 12',
        '4.03*x0 + 7.13*x3 >= 12',
        '4.03*x0 + 6.73*x1 + 5.21*x2 + 7.13*x3 >= 12',
        '9*x0 - 8*x2 >= 0',
        '-4*x1 + 10*x2 >= 0',
        '5.59*x0 + 1.43*x3 <= 96',
        '1.36*x1 + 7.89*x2 <= 39',
        '1.36*x1 + 1.43*x3 <= 98',
        '5.59*x0 + 1.36*x1 + 1.43*x3 <= 67',
        '4.03*x0 + 7.13*x3 <= 41',
        '6.73*x1 + 7.13*x3 <= 122',
        '6.73*x1 + 5.21*x2 + 7.13*x3 <= 75',
        '4.03*x0 + 6.73*x1 + 7.13*x3 <= 99',
        '4.03*x0 + 5.21*x2 + 7.13*x3 <= 87'
    ]
}
```

## Step 5: Write the Gurobi code to solve the optimization problem
```python
import gurobi

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

# Define the variables
x0 = m.addVar(name="fruit_salads", lb=0)
x1 = m.addVar(name="hamburgers", lb=0)
x2 = m.addVar(name="kiwis", lb=0)
x3 = m.addVar(name="chicken_thighs", lb=0)

# Set the objective function
m.setObjective(3*x0 + 3*x1 + x2 + 9*x3, gurobi.GRB.MINIMIZE)

# Add constraints
m.addConstr(5.59*x0 + 1.36*x1 + 7.89*x2 + 1.43*x3 <= 110)
m.addConstr(4.03*x0 + 6.73*x1 + 5.21*x2 + 7.13*x3 <= 122)
m.addConstr(5.59*x0 + 7.89*x2 >= 10)
m.addConstr(1.36*x1 + 1.43*x3 >= 18)
m.addConstr(7.89*x2 + 1.43*x3 >= 10)
m.addConstr(5.59*x0 + 7.89*x2 + 1.43*x3 >= 27)
m.addConstr(5.59*x0 + 1.36*x1 + 7.89*x2 + 1.43*x3 >= 27)
m.addConstr(6.73*x1 + 5.21*x2 >= 24)
m.addConstr(4.03*x0 + 6.73*x1 >= 18)
m.addConstr(6.73*x1 + 7.13*x3 >= 12)
m.addConstr(4.03*x0 + 7.13*x3 >= 12)
m.addConstr(4.03*x0 + 6.73*x1 + 5.21*x2 + 7.13*x3 >= 12)
m.addConstr(9*x0 - 8*x2 >= 0)
m.addConstr(-4*x1 + 10*x2 >= 0)
m.addConstr(5.59*x0 + 1.43*x3 <= 96)
m.addConstr(1.36*x1 + 7.89*x2 <= 39)
m.addConstr(1.36*x1 + 1.43*x3 <= 98)
m.addConstr(5.59*x0 + 1.36*x1 + 1.43*x3 <= 67)
m.addConstr(4.03*x0 + 7.13*x3 <= 41)
m.addConstr(6.73*x1 + 7.13*x3 <= 122)
m.addConstr(6.73*x1 + 5.21*x2 + 7.13*x3 <= 75)
m.addConstr(4.03*x0 + 6.73*x1 + 7.13*x3 <= 99)
m.addConstr(4.03*x0 + 5.21*x2 + 7.13*x3 <= 87)

# Solve the model
m.optimize()

# Print the solution
if m.status == gurobi.GRB.OPTIMAL:
    print("Objective: ", m.objVal)
    print("Fruit Salads: ", x0.varValue)
    print("Hamburgers: ", x1.varValue)
    print("Kiwis: ", x2.varValue)
    print("Chicken Thighs: ", x3.varValue)
else:
    print("The model is infeasible")
```