## Step 1: Define the symbolic representation of the problem
The problem involves maximizing an objective function subject to several constraints. The variables are 'strawberries', 'hot dogs', and 'kiwis', which we can denote as $x_1$, $x_2$, and $x_3$ respectively.

## Step 2: Translate the objective function into symbolic notation
The objective function to maximize is $3.13x_1 + 3.77x_2 + 3.2x_3$.

## 3: List the constraints in symbolic notation
The constraints given are:
- $2x_1 + 28x_2 + 20x_3 \leq 640$ (total cost)
- $20x_1 + 6x_2 + 2x_3 \leq 708$ (total fiber)
- $32x_1 + 19x_2 + 5x_3 \leq 95$ (total tastiness rating)
- $31x_1 + 7x_2 + 30x_3 \leq 652$ (total sourness index)
- $2x_1 + 20x_3 \geq 167$ (minimum spend on strawberries and kiwis)
- $2x_1 + 28x_2 \geq 200$ (minimum spend on strawberries and hot dogs)
- $2x_1 + 28x_2 + 20x_3 \geq 121$ (minimum total spend)
- $2x_1 + 20x_3 \leq 430$ (maximum spend on strawberries and kiwis)
- $2x_1 + 28x_2 \leq 311$ (maximum spend on strawberries and hot dogs)
- $2x_1 + 28x_2 + 20x_3 \leq 311$ (maximum total spend)
- $20x_1 + 2x_3 \leq 289$ (maximum fiber from strawberries and kiwis)
- $6x_2 + 2x_3 \leq 586$ (maximum fiber from hot dogs and kiwis)
- $20x_1 + 6x_2 + 2x_3 \leq 695$ (maximum total fiber)
- $32x_1 + 19x_2 \leq 89$ (maximum tastiness from strawberries and hot dogs)
- $32x_1 + 5x_3 \leq 66$ (maximum tastiness from strawberries and kiwis)
- $19x_2 + 5x_3 \leq 88$ (maximum tastiness from hot dogs and kiwis)
- $32x_1 + 19x_2 + 5x_3 \leq 88$ (maximum total tastiness)
- $31x_1 + 7x_2 \leq 453$ (maximum sourness from strawberries and hot dogs)
- $7x_2 + 30x_3 \leq 567$ (maximum sourness from hot dogs and kiwis)
- $31x_1 + 7x_2 + 30x_3 \leq 567$ (maximum total sourness)

## 4: Create the symbolic representation
```json
{
    'sym_variables': [('x1', 'strawberries'), ('x2', 'hot dogs'), ('x3', 'kiwis')],
    'objective_function': '3.13x1 + 3.77x2 + 3.2x3',
    'constraints': [
        '2x1 + 28x2 + 20x3 <= 640',
        '20x1 + 6x2 + 2x3 <= 708',
        '32x1 + 19x2 + 5x3 <= 95',
        '31x1 + 7x2 + 30x3 <= 652',
        '2x1 + 20x3 >= 167',
        '2x1 + 28x2 >= 200',
        '2x1 + 28x2 + 20x3 >= 121',
        '2x1 + 20x3 <= 430',
        '2x1 + 28x2 <= 311',
        '2x1 + 28x2 + 20x3 <= 311',
        '20x1 + 2x3 <= 289',
        '6x2 + 2x3 <= 586',
        '20x1 + 6x2 + 2x3 <= 695',
        '32x1 + 19x2 <= 89',
        '32x1 + 5x3 <= 66',
        '19x2 + 5x3 <= 88',
        '32x1 + 19x2 + 5x3 <= 88',
        '31x1 + 7x2 <= 453',
        '7x2 + 30x3 <= 567',
        '31x1 + 7x2 + 30x3 <= 567'
    ]
}
```

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

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

# Define the variables
x1 = m.addVar(name="strawberries", lb=0)
x2 = m.addVar(name="hot dogs", lb=0)
x3 = m.addVar(name="kiwis", lb=0)

# Define the objective function
m.setObjective(3.13 * x1 + 3.77 * x2 + 3.2 * x3, gurobi.GRB.MAXIMIZE)

# Add constraints
m.addConstr(2 * x1 + 28 * x2 + 20 * x3 <= 640)
m.addConstr(20 * x1 + 6 * x2 + 2 * x3 <= 708)
m.addConstr(32 * x1 + 19 * x2 + 5 * x3 <= 95)
m.addConstr(31 * x1 + 7 * x2 + 30 * x3 <= 652)
m.addConstr(2 * x1 + 20 * x3 >= 167)
m.addConstr(2 * x1 + 28 * x2 >= 200)
m.addConstr(2 * x1 + 28 * x2 + 20 * x3 >= 121)
m.addConstr(2 * x1 + 20 * x3 <= 430)
m.addConstr(2 * x1 + 28 * x2 <= 311)
m.addConstr(2 * x1 + 28 * x2 + 20 * x3 <= 311)
m.addConstr(20 * x1 + 2 * x3 <= 289)
m.addConstr(6 * x2 + 2 * x3 <= 586)
m.addConstr(20 * x1 + 6 * x2 + 2 * x3 <= 695)
m.addConstr(32 * x1 + 19 * x2 <= 89)
m.addConstr(32 * x1 + 5 * x3 <= 66)
m.addConstr(19 * x2 + 5 * x3 <= 88)
m.addConstr(32 * x1 + 19 * x2 + 5 * x3 <= 88)
m.addConstr(31 * x1 + 7 * x2 <= 453)
m.addConstr(7 * x2 + 30 * x3 <= 567)
m.addConstr(31 * x1 + 7 * x2 + 30 * x3 <= 567)

# Optimize the model
m.optimize()

# Print the solution
if m.status == gurobi.GRB.OPTIMAL:
    print("Objective: ", m.objVal)
    print("Strawberries: ", x1.varValue)
    print("Hot Dogs: ", x2.varValue)
    print("Kiwis: ", x3.varValue)
else:
    print("The model is infeasible")
```