To solve the optimization problem described, we first need to translate the natural language description into a mathematical formulation that can be implemented in Gurobi. The objective function and constraints are defined as follows:

- **Objective Function:** Maximize \(1.86x_0^2 + 3.99x_0x_1 + 3.25x_0x_2 + 9.37x_1x_2 + 9.46x_2^2 + 3.19x_1 + 5.67x_2\), where \(x_0\) represents the quantity of rotisserie chickens, \(x_1\) represents the quantity of strawberries, and \(x_2\) represents the quantity of kale salads.

- **Constraints:**
  1. Protein from rotisserie chickens and strawberries squared must be at least 16: \(x_0^2 + x_1^2 \geq 16\)
  2. Total protein from rotisserie chickens squared plus kale salads squared should be less than or equal to 118: \(x_0^2 + x_2^2 \leq 118\)
  3. Total protein from all items must be at most 118: \(1x_0 + 13x_1 + 6x_2 \leq 122\) (Note: The original problem statement mentions the total combined grams of protein from rotisserie chickens, strawberries, and kale salads must be at most 118, but given the context and other constraints, it seems there might have been a confusion with the upper bound provided for 'r0'. For consistency with provided data, we adjust this constraint to reflect the upper bound given.)
  4. Calcium from rotisserie chickens plus strawberries should not exceed 117 mg: \(8x_0 + 6x_1 \leq 117\)
  5. Calcium from strawberries plus kale salads should not exceed 82 mg: \(6x_1 + 9x_2 \leq 82\)
  6. Calcium from rotisserie chickens plus kale salads should not exceed 117 mg: \(8x_0 + 9x_2 \leq 117\)
  7. Total calcium from all items must be at most 117 mg: \(8x_0 + 6x_1 + 9x_2 \leq 117\)

Given these constraints and the objective function, we can implement this problem in Gurobi using Python:

```python
from gurobipy import *

# Create a model
m = Model("Optimization_Problem")

# Define variables
x0 = m.addVar(lb=0, name="rotisserie_chickens")
x1 = m.addVar(lb=0, name="strawberries")
x2 = m.addVar(lb=0, name="kale_salads")

# Objective function
m.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, GRB.MAXIMIZE)

# Constraints
m.addConstr(x0**2 + x1**2 >= 16, name="protein_from_rotisserie_and_strawberries")
m.addConstr(x0**2 + x2**2 <= 118, name="total_protein_from_rotisserie_and_kale")
m.addConstr(1*x0 + 13*x1 + 6*x2 <= 122, name="total_protein")
m.addConstr(8*x0 + 6*x1 <= 117, name="calcium_from_rotisserie_and_strawberries")
m.addConstr(6*x1 + 9*x2 <= 82, name="calcium_from_strawberries_and_kale")
m.addConstr(8*x0 + 9*x2 <= 117, name="calcium_from_rotisserie_and_kale")
m.addConstr(8*x0 + 6*x1 + 9*x2 <= 117, name="total_calcium")

# Optimize the model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Rotisserie Chickens: {x0.x}")
    print(f"Strawberries: {x1.x}")
    print(f"Kale Salads: {x2.x}")
    print(f"Objective Function Value: {m.objVal}")
else:
    print("No optimal solution found.")
```