## Step 1: Define the symbolic representation of the problem
The problem involves three variables: 'kale salads', 'tomatoes', and 'cherry pies', 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 $7x_0 + 1x_1 + 5x_2$.

## 3: List the constraints in symbolic notation
The constraints given are:
- $14x_0 + 9x_1 + 1x_2 \leq 198$ (carbohydrates)
- $13x_0 + 10x_1 + 9x_2 \leq 95$ (tastiness rating)
- $1x_0 + 3x_1 + 1x_2 \leq 127$ (sourness index)
- $7x_0 + 13x_1 + 2x_2 \leq 64$ (umami index)
- $14x_0 + 9x_1 \geq 25$ (carbohydrates from kale salads and tomatoes)
- $14x_0 + 1x_2 \geq 29$ (carbohydrates from kale salads and cherry pies)
- $14x_0 + 9x_1 + 1x_2 \geq 54$ (carbohydrates from all)
- $13x_0 + 10x_1 \geq 18$ (tastiness rating from kale salads and tomatoes)
- $10x_1 + 9x_2 \geq 30$ (tastiness rating from tomatoes and cherry pies)
- $13x_0 + 10x_1 + 9x_2 \geq 20$ (tastiness rating from all)
- $1x_0 + 1x_2 \geq 18$ (sourness index from kale salads and cherry pies)
- $1x_0 + 3x_1 \geq 33$ (sourness index from kale salads and tomatoes)
- $1x_0 + 3x_1 + 1x_2 \geq 33$ (sourness index from all)
- $13x_1 + 2x_2 \geq 12$ (umami index from tomatoes and cherry pies)
- $7x_0 + 2x_2 \geq 19$ (umami index from kale salads and cherry pies)
- $7x_0 + 13x_1 + 2x_2 \geq 19$ (umami index from all)
- $-x_0 + 9x_1 \geq 0$
- $13x_0 + 9x_2 \leq 91$ (tastiness rating from kale salads and cherry pies)
- $7x_0 + 2x_2 \leq 51$ (umami index from kale salads and cherry pies)
- $13x_1 + 2x_2 \leq 49$ (umami index from tomatoes and cherry pies)
- $7x_0 + 13x_1 + 2x_2 \leq 38$ (umami index from all)
- $x_1 \in \mathbb{Z}$ (integer amount of tomatoes)
- $x_2 \in \mathbb{Z}$ (integer amount of cherry pies)
- $x_0 \in \mathbb{R}$ (non-whole number of kale salads)

## 4: Provide the symbolic representation in the required format
```json
{
    'sym_variables': [('x0', 'kale salads'), ('x1', 'tomatoes'), ('x2', 'cherry pies')],
    'objective_function': '7*x0 + 1*x1 + 5*x2',
    'constraints': [
        '14*x0 + 9*x1 + 1*x2 <= 198',
        '13*x0 + 10*x1 + 9*x2 <= 95',
        '1*x0 + 3*x1 + 1*x2 <= 127',
        '7*x0 + 13*x1 + 2*x2 <= 64',
        '14*x0 + 9*x1 >= 25',
        '14*x0 + 1*x2 >= 29',
        '14*x0 + 9*x1 + 1*x2 >= 54',
        '13*x0 + 10*x1 >= 18',
        '10*x1 + 9*x2 >= 30',
        '13*x0 + 10*x1 + 9*x2 >= 20',
        '1*x0 + 1*x2 >= 18',
        '1*x0 + 3*x1 >= 33',
        '1*x0 + 3*x1 + 1*x2 >= 33',
        '13*x1 + 2*x2 >= 12',
        '7*x0 + 2*x2 >= 19',
        '7*x0 + 13*x1 + 2*x2 >= 19',
        '-1*x0 + 9*x1 >= 0',
        '13*x0 + 9*x2 <= 91',
        '7*x0 + 2*x2 <= 51',
        '13*x1 + 2*x2 <= 49',
        '7*x0 + 13*x1 + 2*x2 <= 38'
    ]
}
```

## 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="kale_salads", lb=0, ub=None)  # non-whole number of kale salads
x1 = m.addVar(name="tomatoes", lb=0, ub=None, integrality=1)  # integer amount of tomatoes
x2 = m.addVar(name="cherry_pies", lb=0, ub=None, integrality=1)  # integer amount of cherry pies

# Define the objective function
m.setObjective(7 * x0 + 1 * x1 + 5 * x2, gurobi.GRB.MINIMIZE)

# Add constraints
m.addConstr(14 * x0 + 9 * x1 + 1 * x2 <= 198)
m.addConstr(13 * x0 + 10 * x1 + 9 * x2 <= 95)
m.addConstr(1 * x0 + 3 * x1 + 1 * x2 <= 127)
m.addConstr(7 * x0 + 13 * x1 + 2 * x2 <= 64)
m.addConstr(14 * x0 + 9 * x1 >= 25)
m.addConstr(14 * x0 + 1 * x2 >= 29)
m.addConstr(14 * x0 + 9 * x1 + 1 * x2 >= 54)
m.addConstr(13 * x0 + 10 * x1 >= 18)
m.addConstr(10 * x1 + 9 * x2 >= 30)
m.addConstr(13 * x0 + 10 * x1 + 9 * x2 >= 20)
m.addConstr(1 * x0 + 1 * x2 >= 18)
m.addConstr(1 * x0 + 3 * x1 >= 33)
m.addConstr(1 * x0 + 3 * x1 + 1 * x2 >= 33)
m.addConstr(13 * x1 + 2 * x2 >= 12)
m.addConstr(7 * x0 + 2 * x2 >= 19)
m.addConstr(7 * x0 + 13 * x1 + 2 * x2 >= 19)
m.addConstr(-1 * x0 + 9 * x1 >= 0)
m.addConstr(13 * x0 + 9 * x2 <= 91)
m.addConstr(7 * x0 + 2 * x2 <= 51)
m.addConstr(13 * x1 + 2 * x2 <= 49)
m.addConstr(7 * x0 + 13 * x1 + 2 * x2 <= 38)

# 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("Cherry Pies: ", x2.varValue)
else:
    print("The model is infeasible")
```