## Step 1: Define the symbolic representation of the variables
The variables are 'kale salads', 'rotisserie chickens', 'potatoes', 'blueberry pies' which can be represented 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 $3 \times \text{kale salads} + 7 \times \text{rotisserie chickens} + 7 \times \text{potatoes} + 5 \times \text{blueberry pies}$, which translates to $3x_0 + 7x_1 + 7x_2 + 5x_3$.

## 3: List the constraints in symbolic notation
1. $\text{sourness index of kale salads} = 8x_0$
2. $\text{tastiness rating of kale salads} = 7x_0$
3. $\text{sourness index of rotisserie chickens} = 8x_1$
4. $\text{tastiness rating of rotisserie chickens} = 2x_1$
5. $\text{sourness index of potatoes} = 2x_2$
6. $\text{tastiness rating of potatoes} = 7x_2$
7. $\text{sourness index of blueberry pies} = 6x_3$
8. $\text{tastiness rating of blueberry pies} = 2x_3$

## 4: Translate given constraints into algebraic form
- $8x_0 + 2x_2 \geq 3$
- $2x_2 + 6x_3 \geq 6$
- $8x_1 + 6x_3 \geq 8$
- $8x_0 + 8x_1 \geq 10$
- $8x_0 + 8x_1 + 2x_2 + 6x_3 \geq 10$
- $7x_0 + 7x_2 \geq 10$
- $2x_1 + 2x_3 \geq 18$
- $2x_1 + 7x_2 \geq 9$
- $7x_0 + 2x_3 \geq 15$
- $7x_0 + 2x_1 + 2x_3 \geq 25$
- $7x_0 + 2x_1 + 7x_2 + 2x_3 \geq 25$
- $2x_0 - x_2 \geq 0$
- $10x_1 - 6x_2 \geq 0$
- $8x_0 + 2x_2 \leq 21$
- $8x_0 + 8x_1 \leq 12$
- $8x_0 + 8x_1 + 2x_2 \leq 12$
- $8x_0 + 2x_2 + 6x_3 \leq 43$
- $2x_1 + 2x_3 \leq 90$
- $7x_0 + 2x_1 + 2x_3 \leq 47$

## 5: Define the symbolic representation of the problem
```json
{
    'sym_variables': [
        ('x0', 'kale salads'), 
        ('x1', 'rotisserie chickens'), 
        ('x2', 'potatoes'), 
        ('x3', 'blueberry pies')
    ], 
    'objective_function': '3*x0 + 7*x1 + 7*x2 + 5*x3', 
    'constraints': [
        '8*x0 + 2*x2 >= 3',
        '2*x2 + 6*x3 >= 6',
        '8*x1 + 6*x3 >= 8',
        '8*x0 + 8*x1 >= 10',
        '8*x0 + 8*x1 + 2*x2 + 6*x3 >= 10',
        '7*x0 + 7*x2 >= 10',
        '2*x1 + 2*x3 >= 18',
        '2*x1 + 7*x2 >= 9',
        '7*x0 + 2*x3 >= 15',
        '7*x0 + 2*x1 + 2*x3 >= 25',
        '7*x0 + 2*x1 + 7*x2 + 2*x3 >= 25',
        '2*x0 - x2 >= 0',
        '10*x1 - 6*x2 >= 0',
        '8*x0 + 2*x2 <= 21',
        '8*x0 + 8*x1 <= 12',
        '8*x0 + 8*x1 + 2*x2 <= 12',
        '8*x0 + 2*x2 + 6*x3 <= 43',
        '2*x1 + 2*x3 <= 90',
        '7*x0 + 2*x1 + 2*x3 <= 47'
    ]
}
```

## 6: Write the Gurobi code for the optimization problem
```python
import gurobi

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

# Define the variables
x0 = m.addVar(name="kale_salads", vtype=gurobi.GRB.CONTINUOUS)
x1 = m.addVar(name="rotisserie_chickens", vtype=gurobi.GRB.INTEGER)
x2 = m.addVar(name="potatoes", vtype=gurobi.GRB.INTEGER)
x3 = m.addVar(name="blueberry_pies", vtype=gurobi.GRB.INTEGER)

# Define the objective function
m.setObjective(3*x0 + 7*x1 + 7*x2 + 5*x3, gurobi.GRB.MINIMIZE)

# Add constraints
m.addConstr(8*x0 + 2*x2 >= 3)
m.addConstr(2*x2 + 6*x3 >= 6)
m.addConstr(8*x1 + 6*x3 >= 8)
m.addConstr(8*x0 + 8*x1 >= 10)
m.addConstr(8*x0 + 8*x1 + 2*x2 + 6*x3 >= 10)
m.addConstr(7*x0 + 7*x2 >= 10)
m.addConstr(2*x1 + 2*x3 >= 18)
m.addConstr(2*x1 + 7*x2 >= 9)
m.addConstr(7*x0 + 2*x3 >= 15)
m.addConstr(7*x0 + 2*x1 + 2*x3 >= 25)
m.addConstr(7*x0 + 2*x1 + 7*x2 + 2*x3 >= 25)
m.addConstr(2*x0 - x2 >= 0)
m.addConstr(10*x1 - 6*x2 >= 0)
m.addConstr(8*x0 + 2*x2 <= 21)
m.addConstr(8*x0 + 8*x1 <= 12)
m.addConstr(8*x0 + 8*x1 + 2*x2 <= 12)
m.addConstr(8*x0 + 2*x2 + 6*x3 <= 43)
m.addConstr(2*x1 + 2*x3 <= 90)
m.addConstr(7*x0 + 2*x1 + 2*x3 <= 47)

# Solve the model
m.optimize()

# Print the solution
if m.status == gurobi.GRB.OPTIMAL:
    print("Objective: ", m.objVal)
    print("Kale Salads: ", x0.varValue)
    print("Rotisserie Chickens: ", x1.varValue)
    print("Potatoes: ", x2.varValue)
    print("Blueberry Pies: ", x3.varValue)
else:
    print("The model is infeasible")
```