## Step 1: Define the symbolic representation of the variables
The variables are 'kale salads', 'tomatoes', and 'strips of bacon', which we will denote as $x_0$, $x_1$, and $x_2$ respectively.

## Step 2: Convert the objective function into a symbolic representation
The objective function to minimize is $9x_0 + 1x_1 + 6x_2$.

## Step 3: Define the constraints in symbolic notation
1. $3x_0 + 14x_1 + 2x_2 \geq 20$ (tastiness rating from kale salads and tomatoes)
2. $14x_1 + 2x_2 \geq 13$ (tastiness rating from tomatoes and strips of bacon)
3. $3x_0 + 14x_1 + 2x_2 \geq 21$ (total tastiness rating from all)
4. $3x_0 + 14x_1 + 2x_2 \geq 21$ (same as above, redundant)
5. $8x_0 + 13x_1 \geq 51$ (calcium from kale salads and tomatoes)
6. $13x_1 + 8x_2 \geq 45$ (calcium from tomatoes and strips of bacon)
7. $8x_0 + 8x_2 \geq 21$ (calcium from kale salads and strips of bacon)
8. $8x_0 + 13x_1 + 8x_2 \geq 21$ (calcium from all)
9. $7x_0 + 5x_1 \geq 57$ (protein from kale salads and tomatoes)
10. $7x_0 + 5x_1 + 3x_2 \geq 57$ (protein from all)
11. $-5x_1 + 4x_2 \geq 0$ (relationship between tomatoes and strips of bacon)
12. $-6x_0 + 9x_1 \geq 0$ (relationship between kale salads and tomatoes)
13. $3x_0 + 14x_1 + 2x_2 \leq 48$ (upper limit on total tastiness rating from kale salads and tomatoes)
14. $x_0, x_1, x_2 \geq 0$ and are integers (non-negativity and integrality constraints)

## 4: Provide the symbolic representation in JSON format
```json
{
    'sym_variables': [('x0', 'kale salads'), ('x1', 'tomatoes'), ('x2', 'strips of bacon')],
    'objective_function': '9*x0 + 1*x1 + 6*x2',
    'constraints': [
        '3*x0 + 14*x1 + 2*x2 >= 20',
        '14*x1 + 2*x2 >= 13',
        '3*x0 + 14*x1 + 2*x2 >= 21',
        '8*x0 + 13*x1 >= 51',
        '13*x1 + 8*x2 >= 45',
        '8*x0 + 8*x2 >= 21',
        '8*x0 + 13*x1 + 8*x2 >= 21',
        '7*x0 + 5*x1 >= 57',
        '7*x0 + 5*x1 + 3*x2 >= 57',
        '-5*x1 + 4*x2 >= 0',
        '-6*x0 + 9*x1 >= 0',
        '3*x0 + 14*x1 + 2*x2 <= 48',
        'x0, x1, x2 are integers'
    ]
}
```

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

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

# Define the variables
x0 = m.addVar(name="kale_salads", vtype=gurobi.GRB.INTEGER)
x1 = m.addVar(name="tomatoes", vtype=gurobi.GRB.INTEGER)
x2 = m.addVar(name="strips_of_bacon", vtype=gurobi.GRB.INTEGER)

# Set the objective function
m.setObjective(9 * x0 + x1 + 6 * x2, gurobi.GRB.MINIMIZE)

# Add constraints
m.addConstr(3 * x0 + 14 * x1 + 2 * x2 >= 20)
m.addConstr(14 * x1 + 2 * x2 >= 13)
m.addConstr(3 * x0 + 14 * x1 + 2 * x2 >= 21)
m.addConstr(8 * x0 + 13 * x1 >= 51)
m.addConstr(13 * x1 + 8 * x2 >= 45)
m.addConstr(8 * x0 + 8 * x2 >= 21)
m.addConstr(8 * x0 + 13 * x1 + 8 * x2 >= 21)
m.addConstr(7 * x0 + 5 * x1 >= 57)
m.addConstr(7 * x0 + 5 * x1 + 3 * x2 >= 57)
m.addConstr(-5 * x1 + 4 * x2 >= 0)
m.addConstr(-6 * x0 + 9 * x1 >= 0)
m.addConstr(3 * x0 + 14 * x1 + 2 * x2 <= 48)

# Optimize the model
m.optimize()

# Print the solution
if m.status == gurobi.GRB.OPTIMAL:
    print("Objective: ", m.objVal)
    print("Kale Salads: ", x0.varValue)
    print("Tomatoes: ", x1.varValue)
    print("Strips of Bacon: ", x2.varValue)
else:
    print("The model is infeasible")
```