## Step 1: Define the symbolic representation of the variables
The variables are 'chicken thighs', 'apple pies', and 'knishes', which can be represented symbolically as $x_1$, $x_2$, and $x_3$ respectively.

## Step 2: Convert the natural language objects into symbolic notation
The symbolic representation of the variables is:
- $x_1$ for 'chicken thighs'
- $x_2$ for 'apple pies'
- $x_3$ for 'knishes'

## 3: Define the objective function in symbolic notation
The objective function to minimize is $3x_1 + 2x_2 + 6x_3$.

## 4: Define the constraints in symbolic notation
The constraints are:
- $6x_1 + 19x_2 \geq 57$ (at least 57 milligrams of calcium from chicken thighs and apple pies)
- $6x_1 + 17x_3 \geq 59$ (at least 59 milligrams of calcium from chicken thighs and knishes)
- $6x_1 + 19x_2 + 17x_3 \geq 59$ (at least 59 milligrams of calcium from all three)
- $-10x_1 + x_2 \geq 0$ (constraint on chicken thighs and apple pies)

## 5: Represent the problem in the required JSON format
```json
{
    'sym_variables': [('x1', 'chicken thighs'), ('x2', 'apple pies'), ('x3', 'knishes')],
    'objective_function': '3*x1 + 2*x2 + 6*x3',
    'constraints': [
        '6*x1 + 19*x2 >= 57',
        '6*x1 + 17*x3 >= 59',
        '6*x1 + 19*x2 + 17*x3 >= 59',
        '-10*x1 + x2 >= 0'
    ]
}
```

## 6: Implement the optimization problem using Gurobi
```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name="chicken_thighs", lb=-float('inf'))  # No lower bound
    x2 = model.addVar(name="apple_pies", lb=-float('inf'))  # No lower bound
    x3 = model.addVar(name="knishes", lb=-float('inf'))  # No lower bound

    # Define the objective function
    model.setObjective(3*x1 + 2*x2 + 6*x3, gurobi.GRB.MINIMIZE)

    # Define the constraints
    model.addConstr(6*x1 + 19*x2 >= 57)
    model.addConstr(6*x1 + 17*x3 >= 59)
    model.addConstr(6*x1 + 19*x2 + 17*x3 >= 59)
    model.addConstr(-10*x1 + x2 >= 0)

    # Optimize the model
    model.optimize()

    # Check if the model is optimized
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Chicken thighs: {x1.x}")
        print(f"Apple pies: {x2.x}")
        print(f"Knishes: {x3.x}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```