## Step 1: Define the symbolic representation of the problem
The problem involves maximizing an objective function subject to several constraints. The variables are 'pickles', 'chicken thighs', and 'strips of bacon', which we will denote as $x_0$, $x_1$, and $x_2$ respectively.

## Step 2: Translate the objective function into symbolic notation
The objective function to maximize is: $2x_0^2 + 4x_0x_1 + x_0x_2 + 6x_1^2 + 7x_1x_2 + 8x_2^2 + 5x_0 + 7x_1 + 3x_2$.

## 3: List the constraints in symbolic notation
The constraints are:
- $x_0^2 + x_1^2 + x_2^2 \geq 23$ (total combined grams of protein)
- $3.04x_1 + 6.13x_2 \geq 20$ (milligrams of iron from chicken thighs and strips of bacon)
- $0.34x_0 + 6.13x_2 \geq 29$ (milligrams of iron from pickles and strips of bacon)
- $4.7x_0^2 + 0.92x_1^2 + 2.52x_2^2 \geq 30$ (dollar cost constraint)
- $4x_0 - 4x_2 \geq 0$ (relationship between pickles and strips of bacon)
- $x_0^2 + x_2^2 \leq 85$ (grams of protein constraint)
- $x_1^2 + x_2^2 \leq 71$ (grams of protein constraint)
- $x_0^2 + x_1^2 + x_2^2 \leq 64$ (grams of protein constraint)
- $2.39x_0 + 6.83x_1 + 1.18x_2 \leq 64$ (total grams of protein)
- $0.34x_0 + 6.13x_2 \leq 95$ (iron from pickles and strips of bacon)
- $3.04x_1 + 6.13x_2 \leq 96$ (iron from chicken thighs and strips of bacon)
- $2.39x_0 + 3.04x_1 + 6.13x_2 \leq 96$ (total iron)
- $1.02x_0^2 + 0.07x_2^2 \leq 81$ (carbohydrates from pickles and strips of bacon)
- $1.02x_0 + 2.81x_1 \leq 86$ (carbohydrates from pickles and chicken thighs)
- $1.02x_0 + 2.81x_1 + 0.07x_2 \leq 86$ (total carbohydrates)
- $4.7x_0^2 + 2.52x_2^2 \leq 82$ (cost constraint)
- $0.92x_1^2 + 2.52x_2^2 \leq 86$ (cost constraint)
- $4.7x_0 + 0.92x_1 + 2.52x_2 \leq 86$ (total cost)

## 4: Define the symbolic variables and constraints for the output
```json
{
    'sym_variables': [
        ('x0', 'pickles'), 
        ('x1', 'chicken thighs'), 
        ('x2', 'strips of bacon')
    ], 
    'objective_function': '2*x0^2 + 4*x0*x1 + x0*x2 + 6*x1^2 + 7*x1*x2 + 8*x2^2 + 5*x0 + 7*x1 + 3*x2', 
    'constraints': [
        'x0^2 + x1^2 + x2^2 >= 23',
        '3.04*x1 + 6.13*x2 >= 20',
        '0.34*x0 + 6.13*x2 >= 29',
        '4.7*x0^2 + 0.92*x1^2 + 2.52*x2^2 >= 30',
        '4*x0 - 4*x2 >= 0',
        'x0^2 + x2^2 <= 85',
        'x1^2 + x2^2 <= 71',
        'x0^2 + x1^2 + x2^2 <= 64',
        '2.39*x0 + 6.83*x1 + 1.18*x2 <= 64',
        '0.34*x0 + 6.13*x2 <= 95',
        '3.04*x1 + 6.13*x2 <= 96',
        '2.39*x0 + 3.04*x1 + 6.13*x2 <= 96',
        '1.02*x0^2 + 0.07*x2^2 <= 81',
        '1.02*x0 + 2.81*x1 <= 86',
        '1.02*x0 + 2.81*x1 + 0.07*x2 <= 86',
        '4.7*x0^2 + 2.52*x2^2 <= 82',
        '0.92*x1^2 + 2.52*x2^2 <= 86',
        '4.7*x0 + 0.92*x1 + 2.52*x2 <= 86'
    ]
}
```

## 5: Write the Gurobi code to solve the optimization problem
```python
import gurobi

# Define the model
m = gurobi.Model()

# Define the variables
x0 = m.addVar(name="pickles", vtype=gurobi.GRB.CONTINUOUS)
x1 = m.addVar(name="chicken thighs", vtype=gurobi.GRB.INTEGER)
x2 = m.addVar(name="strips of bacon", vtype=gurobi.GRB.INTEGER)

# Define the objective function
m.setObjective(2*x0**2 + 4*x0*x1 + x0*x2 + 6*x1**2 + 7*x1*x2 + 8*x2**2 + 5*x0 + 7*x1 + 3*x2, gurobi.GRB.MAXIMIZE)

# Define the constraints
m.addConstr(x0**2 + x1**2 + x2**2 >= 23)
m.addConstr(3.04*x1 + 6.13*x2 >= 20)
m.addConstr(0.34*x0 + 6.13*x2 >= 29)
m.addConstr(4.7*x0**2 + 0.92*x1**2 + 2.52*x2**2 >= 30)
m.addConstr(4*x0 - 4*x2 >= 0)
m.addConstr(x0**2 + x2**2 <= 85)
m.addConstr(x1**2 + x2**2 <= 71)
m.addConstr(x0**2 + x1**2 + x2**2 <= 64)
m.addConstr(2.39*x0 + 6.83*x1 + 1.18*x2 <= 64)
m.addConstr(0.34*x0 + 6.13*x2 <= 95)
m.addConstr(3.04*x1 + 6.13*x2 <= 96)
m.addConstr(2.39*x0 + 3.04*x1 + 6.13*x2 <= 96)
m.addConstr(1.02*x0**2 + 0.07*x2**2 <= 81)
m.addConstr(1.02*x0 + 2.81*x1 <= 86)
m.addConstr(1.02*x0 + 2.81*x1 + 0.07*x2 <= 86)
m.addConstr(4.7*x0**2 + 2.52*x2**2 <= 82)
m.addConstr(0.92*x1**2 + 2.52*x2**2 <= 86)
m.addConstr(4.7*x0 + 0.92*x1 + 2.52*x2 <= 86)

# Solve the model
m.optimize()

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