## Step 1: Define the symbolic representation of the problem
First, let's define the symbolic variables and the objective function based on the given problem description.

## Step 2: Identify the variables and their corresponding symbolic representations
The variables are 'milkshakes', 'green beans', and 'chicken thighs', which we can represent symbolically as $x_0$, $x_1$, and $x_2$ respectively.

## 3: Formulate the objective function
The objective function to minimize is $1 \times \text{milkshakes} + 8 \times \text{green beans} + 1 \times \text{chicken thighs}$, which in symbolic terms is $x_0 + 8x_1 + x_2$.

## 4: List the constraints
The constraints given are:
1. $9x_0 + 3x_1 + 28x_2 \leq 89$ (iron upper bound)
2. $17x_0 + 13x_1 + 4x_2 \leq 150$ (umami index upper bound)
3. $9x_0 + 3x_1 \geq 24$ (iron from milkshakes and green beans)
4. $3x_1 + 28x_2 \geq 18$ (iron from green beans and chicken thighs)
5. $9x_0 + 3x_1 + 28x_2 \geq 18$ (iron from all sources)
6. $17x_0 + 13x_1 \geq 46$ (umami from milkshakes and green beans)
7. $17x_0 + 4x_2 \geq 38$ (umami from milkshakes and chicken thighs)
8. $13x_1 + 4x_2 \geq 31$ (umami from green beans and chicken thighs)
9. $17x_0 + 13x_1 + 4x_2 \geq 31$ (umami from all sources)
10. $6x_0 - 10x_2 \geq 0$ (milkshakes and chicken thighs constraint)
11. $-9x_0 + 10x_1 \geq 0$ (milkshakes and green beans constraint)
12. $9x_0 + 3x_1 + 28x_2 \leq 56$ (iron lower bound from all sources)
13. $13x_1 + 4x_2 \leq 85$ (umami upper bound from green beans and chicken thighs)
14. $17x_0 + 4x_2 \leq 108$ (umami upper bound from milkshakes and chicken thighs)

## 5: Symbolic representation of the problem
The symbolic representation is:
```json
{
    'sym_variables': [('x0', 'milkshakes'), ('x1', 'green beans'), ('x2', 'chicken thighs')],
    'objective_function': 'x0 + 8x1 + x2',
    'constraints': [
        '9x0 + 3x1 + 28x2 <= 89',
        '17x0 + 13x1 + 4x2 <= 150',
        '9x0 + 3x1 >= 24',
        '3x1 + 28x2 >= 18',
        '9x0 + 3x1 + 28x2 >= 18',
        '17x0 + 13x1 >= 46',
        '17x0 + 4x2 >= 38',
        '13x1 + 4x2 >= 31',
        '17x0 + 13x1 + 4x2 >= 31',
        '6x0 - 10x2 >= 0',
        '-9x0 + 10x1 >= 0',
        '9x0 + 3x1 + 28x2 <= 56',
        '13x1 + 4x2 <= 85',
        '17x0 + 4x2 <= 108'
    ]
}
```

## 6: Gurobi code for the optimization problem
Now, let's write the Gurobi code to solve this problem:
```python
import gurobi

def solve_optimization_problem():
    # Create a new model
    model = gurobi.Model()

    # Define the variables
    milkshakes = model.addVar(lb=0, name="milkshakes", vtype=gurobi.GRB.CONTINUOUS)
    green_beans = model.addVar(lb=0, name="green_beans", vtype=gurobi.GRB.CONTINUOUS)
    chicken_thighs = model.addVar(lb=0, name="chicken_thighs", vtype=gurobi.GRB.CONTINUOUS)

    # Objective function
    model.setObjective(milkshakes + 8 * green_beans + chicken_thighs, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(9 * milkshakes + 3 * green_beans + 28 * chicken_thighs <= 89)
    model.addConstr(17 * milkshakes + 13 * green_beans + 4 * chicken_thighs <= 150)
    model.addConstr(9 * milkshakes + 3 * green_beans >= 24)
    model.addConstr(3 * green_beans + 28 * chicken_thighs >= 18)
    model.addConstr(9 * milkshakes + 3 * green_beans + 28 * chicken_thighs >= 18)
    model.addConstr(17 * milkshakes + 13 * green_beans >= 46)
    model.addConstr(17 * milkshakes + 4 * chicken_thighs >= 38)
    model.addConstr(13 * green_beans + 4 * chicken_thighs >= 31)
    model.addConstr(17 * milkshakes + 13 * green_beans + 4 * chicken_thighs >= 31)
    model.addConstr(6 * milkshakes - 10 * chicken_thighs >= 0)
    model.addConstr(-9 * milkshakes + 10 * green_beans >= 0)
    model.addConstr(9 * milkshakes + 3 * green_beans + 28 * chicken_thighs <= 56)
    model.addConstr(13 * green_beans + 4 * chicken_thighs <= 85)
    model.addConstr(17 * milkshakes + 4 * chicken_thighs <= 108)

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Milkshakes: {milkshakes.varValue}")
        print(f"Green beans: {green_beans.varValue}")
        print(f"Chicken thighs: {chicken_thighs.varValue}")
        print(f"Objective: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```