## Problem Description and Symbolic Representation

The problem requires finding the optimal number of SD and LD pills John should buy to minimize his cost while meeting his daily calcium and iron requirements.

### Symbolic Representation

Let's define the symbolic variables:
- $x_1$ represents the number of SD pills
- $x_2$ represents the number of LD pills

The objective function to minimize is the total cost: $1x_1 + 1.5x_2$

The constraints based on the requirements are:
- Calcium: $1x_1 + 2x_2 \geq 15$
- Iron: $4x_1 + 1x_2 \geq 20$
- Non-negativity: $x_1 \geq 0, x_2 \geq 0$

## Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'SD pills'), ('x2', 'LD pills')],
    'objective_function': '1*x1 + 1.5*x2',
    'constraints': [
        '1*x1 + 2*x2 >= 15', 
        '4*x1 + 1*x2 >= 20', 
        'x1 >= 0', 
        'x2 >= 0'
    ]
}
```

## Gurobi Code in Python

```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name='x1', lb=0, vtype=gurobi.GRB.CONTINUOUS)  # SD pills
    x2 = model.addVar(name='x2', lb=0, vtype=gurobi.GRB.CONTINUOUS)  # LD pills

    # Objective function: Minimize 1*x1 + 1.5*x2
    model.setObjective(1*x1 + 1.5*x2, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(1*x1 + 2*x2 >= 15, name='calcium_constraint')
    model.addConstr(4*x1 + 1*x2 >= 20, name='iron_constraint')

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print(f"Optimal solution: x1 = {x1.varValue}, x2 = {x2.varValue}")
        print(f"Minimum cost: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```