## Step 1: Define the symbolic representation of the variables
The variables are 'rotisserie chickens', 'strawberries', and 'kale salads', which can be represented symbolically as $x_0$, $x_1$, and $x_2$ respectively.

## Step 2: Convert the natural language objects into symbolic notation
The symbolic representation of the variables is:
- $x_0$ : 'rotisserie chickens'
- $x_1$ : 'strawberries'
- $x_2$ : 'kale salads'

## 3: Define the objective function in symbolic notation
The objective function to maximize is: $1.86x_0^2 + 3.99x_0x_1 + 3.25x_0x_2 + 9.37x_1x_2 + 9.46x_2^2 + 3.19x_1 + 5.67x_2$

## 4: List the constraints in symbolic notation
The constraints are:
- $x_0 \geq 0$
- $x_1 \geq 0$
- $x_2 \geq 0$
- $x_0 \leq \infty$ (implicitly, as there are upper bounds on resources)
- $x_1 \leq \infty$ (implicitly, as there are upper bounds on resources)
- $x_2 \leq \infty$ (implicitly, as there are upper bounds on resources)
- $x_0 + 13x_1 + 6x_2 \leq 122$ (total protein constraint)
- $8x_0 + 6x_1 + 9x_2 \leq 117$ (total calcium constraint)
- $x_0^2 + x_1^2 \geq 16$ (protein from rotisserie chickens and strawberries)
- $x_0^2 + x_2^2 \leq 118$ (protein from rotisserie chickens and kale salads)
- $x_0 + 13x_1 + 6x_2 \leq 118$ (total protein from all sources)
- $8x_0 + 6x_1 \leq 117$ (calcium from rotisserie chickens and strawberries)
- $6x_1 + 9x_2 \leq 82$ (calcium from strawberries and kale salads)
- $8x_0 + 9x_2 \leq 117$ (calcium from rotisserie chickens and kale salads)
- $8x_0 + 6x_1 + 9x_2 \leq 117$ (total calcium from all sources)

## 5: Create a JSON representation of the problem
```json
{
    'sym_variables': [('x0', 'rotisserie chickens'), ('x1', 'strawberries'), ('x2', 'kale salads')],
    'objective_function': '1.86*x0^2 + 3.99*x0*x1 + 3.25*x0*x2 + 9.37*x1*x2 + 9.46*x2^2 + 3.19*x1 + 5.67*x2',
    'constraints': [
        'x0 >= 0',
        'x1 >= 0',
        'x2 >= 0',
        'x0 + 13*x1 + 6*x2 <= 122',
        '8*x0 + 6*x1 + 9*x2 <= 117',
        'x0^2 + x1^2 >= 16',
        'x0^2 + x2^2 <= 118',
        'x0 + 13*x1 + 6*x2 <= 118',
        '8*x0 + 6*x1 <= 117',
        '6*x1 + 9*x2 <= 82',
        '8*x0 + 9*x2 <= 117',
        '8*x0 + 6*x1 + 9*x2 <= 117'
    ]
}
```

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

def solve_optimization_problem():
    # Create a new Gurobi model
    model = gurobi.Model()

    # Define the variables
    x0 = model.addVar(name='rotisserie_chickens', lb=0, ub=None)
    x1 = model.addVar(name='strawberries', lb=0, ub=None)
    x2 = model.addVar(name='kale_salads', lb=0, ub=None)

    # Define the objective function
    model.setObjective(1.86 * x0**2 + 3.99 * x0 * x1 + 3.25 * x0 * x2 + 9.37 * x1 * x2 + 9.46 * x2**2 + 3.19 * x1 + 5.67 * x2, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(x0 + 13 * x1 + 6 * x2 <= 122, name='protein_constraint')
    model.addConstr(8 * x0 + 6 * x1 + 9 * x2 <= 117, name='calcium_constraint')
    model.addConstr(x0**2 + x1**2 >= 16, name='protein_rotisserie_strawberries')
    model.addConstr(x0**2 + x2**2 <= 118, name='protein_rotisserie_kale')
    model.addConstr(x0 + 13 * x1 + 6 * x2 <= 118, name='total_protein')
    model.addConstr(8 * x0 + 6 * x1 <= 117, name='calcium_rotisserie_strawberries')
    model.addConstr(6 * x1 + 9 * x2 <= 82, name='calcium_strawberries_kale')
    model.addConstr(8 * x0 + 9 * x2 <= 117, name='calcium_rotisserie_kale')
    model.addConstr(8 * x0 + 6 * x1 + 9 * x2 <= 117, name='total_calcium')

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print('Objective: ', model.objVal)
        print('Rotisserie chickens: ', x0.varValue)
        print('Strawberries: ', x1.varValue)
        print('Kale salads: ', x2.varValue)
    else:
        print('No optimal solution found')

solve_optimization_problem()
```