## Step 1: Define the symbolic representation of the problem

Let's denote:
- $x_1$ as the number of blue pills
- $x_2$ as the number of red pills

The objective is to minimize the cost, which is $1x_1 + 2x_2$.

The constraints are:
- $10x_1 + 5x_2 \geq 100$ (at least 100 units of blood pressure medication)
- $3x_1 + 7x_2 \geq 70$ (at least 70 units of diabetes medication)
- $x_1 \geq 0$ and $x_2 \geq 0$ (non-negativity constraints, as the number of pills cannot be negative)

## Step 2: Express the problem in the required symbolic format

```json
{
    'sym_variables': [('x1', 'blue pills'), ('x2', 'red pills')],
    'objective_function': '1*x1 + 2*x2',
    'constraints': [
        '10*x1 + 5*x2 >= 100',
        '3*x1 + 7*x2 >= 70',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## Step 3: Convert the problem into Gurobi code

To solve this linear programming problem using Gurobi, we will use the Gurobi Python API.

```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name="blue_pills", lb=0, ub=gurobi.GRB.INFINITY)
    x2 = model.addVar(name="red_pills", lb=0, ub=gurobi.GRB.INFINITY)

    # Define the objective function
    model.setObjective(1*x1 + 2*x2, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(10*x1 + 5*x2 >= 100, name="blood_pressure_constraint")
    model.addConstr(3*x1 + 7*x2 >= 70, name="diabetes_constraint")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Number of blue pills: {x1.varValue}")
        print(f"Number of red pills: {x2.varValue}")
        print(f"Total cost: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```