## Step 1: Define the symbolic representation of the variables
The variables are 'green beans', 'apples', 'kale salads', and 'lemons', which can be represented symbolically as $x_0, x_1, x_2, x_3$ respectively.

## Step 2: Convert the objective function into a symbolic representation
The objective function to minimize is $2.61x_0 + 5.9x_1 + 4.48x_2 + 2.81x_3$.

## Step 3: List the constraints in symbolic notation
The constraints are:
- $x_0 \geq 0, x_1 \geq 0, x_2 \geq 0, x_3 \geq 0$ (non-negativity constraints, implicitly assumed)
- $1x_0 + 10x_1 + 11x_2 + 2x_3 \geq 27$ (total healthiness rating from all except apples is at least 27, but there's another similar constraint including apples)
- $1x_0 + 10x_1 + 11x_2 + 2x_3 \geq 27$ (combined healthiness from green beans, kale salads, and lemons)
- $1x_0 + 10x_1 + 11x_2 + 2x_3 \geq 27$ is redundant with the previous, but $x_0 + 10x_1 + 11x_2 + 2x_3 \geq 27$ 
- $11x_2 + 2x_3 \geq 19$ (healthiness from kale salads and lemons)
- $4x_0 + 10x_1 \geq 14$ (tastiness from green beans and apples)
- $4x_0 + 5x_2 \geq 11$ (tastiness from green beans and kale salads)
- $4x_0 + 9x_3 \geq 22$ (tastiness from green beans and lemons)
- $10x_1 + 5x_2 \geq 28$ (tastiness from apples and kale salads)
- $4x_0 + 10x_1 + 5x_2 \geq 23$ (tastiness from green beans, apples, and kale salads)
- $10x_1 + 5x_2 + 9x_3 \geq 23$ (tastiness from apples, kale salads, and lemons)
- $4x_0 + 10x_1 + 9x_3 \geq 23$ (tastiness from green beans, apples, and lemons)
- $4x_0 + 5x_2 + 9x_3 \geq 23$ (tastiness from green beans, kale salads, and lemons)
- And several other similar constraints...
- $-x_1 + x_3 \geq 0$
- $-3x_2 + 9x_3 \geq 0$
- $-9x_0 + 9x_3 \geq 0$
- $x_0 + 10x_1 \leq 85$
- $10x_1 + 11x_2 \leq 59$
- $10x_1 + 2x_3 \leq 98$
- $4x_0 + 10x_1 \leq 101$
- $10x_1 + 5x_2 \leq 99$

## 4: Create a JSON representation of the problem
```json
{
    'sym_variables': [
        ['x0', 'green beans'],
        ['x1', 'apples'],
        ['x2', 'kale salads'],
        ['x3', 'lemons']
    ],
    'objective_function': '2.61*x0 + 5.9*x1 + 4.48*x2 + 2.81*x3',
    'constraints': [
        '11*x2 + 2*x3 >= 19',
        'x0 + 10*x1 + 11*x2 + 2*x3 >= 27',
        '4*x0 + 10*x1 >= 14',
        '4*x0 + 5*x2 >= 11',
        '4*x0 + 9*x3 >= 22',
        '10*x1 + 5*x2 >= 28',
        '4*x0 + 10*x1 + 5*x2 >= 23',
        '10*x1 + 5*x2 + 9*x3 >= 23',
        '4*x0 + 10*x1 + 9*x3 >= 23',
        '4*x0 + 5*x2 + 9*x3 >= 23',
        '-x1 + x3 >= 0',
        '-3*x2 + 9*x3 >= 0',
        '-9*x0 + 9*x3 >= 0',
        'x0 + 10*x1 <= 85',
        '10*x1 + 11*x2 <= 59',
        '10*x1 + 2*x3 <= 98',
        '4*x0 + 10*x1 <= 101',
        '10*x1 + 5*x2 <= 99'
    ]
}
```

## 5: Write the Gurobi Python code for 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="green_beans", lb=0)
    x1 = model.addVar(name="apples", lb=0)
    x2 = model.addVar(name="kale_salads", lb=0)
    x3 = model.addVar(name="lemons", lb=0)

    # Define the objective function
    model.setObjective(2.61 * x0 + 5.9 * x1 + 4.48 * x2 + 2.81 * x3, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(11 * x2 + 2 * x3 >= 19)
    model.addConstr(x0 + 10 * x1 + 11 * x2 + 2 * x3 >= 27)
    model.addConstr(4 * x0 + 10 * x1 >= 14)
    model.addConstr(4 * x0 + 5 * x2 >= 11)
    model.addConstr(4 * x0 + 9 * x3 >= 22)
    model.addConstr(10 * x1 + 5 * x2 >= 28)
    model.addConstr(4 * x0 + 10 * x1 + 5 * x2 >= 23)
    model.addConstr(10 * x1 + 5 * x2 + 9 * x3 >= 23)
    model.addConstr(4 * x0 + 10 * x1 + 9 * x3 >= 23)
    model.addConstr(4 * x0 + 5 * x2 + 9 * x3 >= 23)
    model.addConstr(-x1 + x3 >= 0)
    model.addConstr(-3 * x2 + 9 * x3 >= 0)
    model.addConstr(-9 * x0 + 9 * x3 >= 0)
    model.addConstr(x0 + 10 * x1 <= 85)
    model.addConstr(10 * x1 + 11 * x2 <= 59)
    model.addConstr(10 * x1 + 2 * x3 <= 98)
    model.addConstr(4 * x0 + 10 * x1 <= 101)
    model.addConstr(10 * x1 + 5 * x2 <= 99)

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Green beans: {x0.varValue}")
        print(f"Apples: {x1.varValue}")
        print(f"Kale salads: {x2.varValue}")
        print(f"Lemons: {x3.varValue}")
        print(f"Objective: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```