## Step 1: Define the symbolic representation of the variables
The variables are 'fruit salads', 'pickles', 'chicken breasts', and 'slices of pizza'. Let's denote them as $x_1$, $x_2$, $x_3$, and $x_4$ respectively.

## Step 2: Convert the objective function into a symbolic representation
The objective function to maximize is $8.42x_1^2 + 6.59x_1x_2 + 4.82x_1x_3 + 7.22x_1x_4 + 1.9x_2^2 + 5.91x_2x_3 + 4.85x_3^2 + 4.59x_4^2 + 5.64x_2 + 4.36x_4$.

## 3: Define the constraints in symbolic notation
The constraints are:
- $10x_1 + 22x_2 + 3x_3 + 24x_4 \leq 356$
- $22x_2 + 24x_4 \leq 464$
- $10x_1 + 3x_3 \leq 205$
- $x_4$ is an integer

## 4: Express the problem in the required JSON format
```json
{
    'sym_variables': [
        ('x1', 'fruit salads'), 
        ('x2', 'pickles'), 
        ('x3', 'chicken breasts'), 
        ('x4', 'slices of pizza')
    ], 
    'objective_function': '8.42*x1^2 + 6.59*x1*x2 + 4.82*x1*x3 + 7.22*x1*x4 + 1.9*x2^2 + 5.91*x2*x3 + 4.85*x3^2 + 4.59*x4^2 + 5.64*x2 + 4.36*x4', 
    'constraints': [
        '10*x1 + 22*x2 + 3*x3 + 24*x4 <= 356', 
        '22*x2 + 24*x4 <= 464', 
        '10*x1 + 3*x3 <= 205', 
        'x4 == int(x4)'
    ]
}
```

## Step 5: Implement the optimization problem using Gurobi
```python
import gurobi as gp

# Define the model
m = gp.Model()

# Define the variables
x1 = m.addVar(name="fruit_salads", lb=0)  # No lower bound specified, assuming 0
x2 = m.addVar(name="pickles", lb=0)     # No lower bound specified, assuming 0
x3 = m.addVar(name="chicken_breasts", lb=0)  # No lower bound specified, assuming 0
x4 = m.addVar(name="slices_of_pizza", lb=0, vtype=gp.GRB.INTEGER)

# Define the objective function
m.setObjective(8.42*x1**2 + 6.59*x1*x2 + 4.82*x1*x3 + 7.22*x1*x4 + 
               1.9*x2**2 + 5.91*x2*x3 + 4.85*x3**2 + 4.59*x4**2 + 
               5.64*x2 + 4.36*x4, gp.GRB.MAXIMIZE)

# Define the constraints
m.addConstr(10*x1 + 22*x2 + 3*x3 + 24*x4 <= 356)
m.addConstr(22*x2 + 24*x4 <= 464)
m.addConstr(10*x1 + 3*x3 <= 205)

# Optimize the model
m.optimize()

# Print the solution
if m.status == gp.GRB.OPTIMAL:
    print("Objective: ", m.objVal)
    print("Fruit Salads: ", x1.varValue)
    print("Pickles: ", x2.varValue)
    print("Chicken Breasts: ", x3.varValue)
    print("Slices of Pizza: ", x4.varValue)
else:
    print("The model is infeasible or unbounded.")
```