## Step 1: Define the symbolic representation of the problem
Let's denote the number of bottles of pills as $x_1$ and the number of bottles of cream as $x_2$. The profit per bottle of pills is $50, and the profit per bottle of cream is $60. The objective is to maximize the total profit.

## Step 2: Formulate the objective function
The objective function to maximize is $50x_1 + 60x_2$.

## 3: Define the constraints based on the given resources
Each bottle of pills takes 20 minutes of weighing, and each bottle of cream takes 15 minutes of weighing. The pharmacy has 4000 minutes available for weighing. This gives us the constraint $20x_1 + 15x_2 \leq 4000$.

## 4: Define the packaging constraints
Each bottle of pills takes 10 minutes of packaging, and each bottle of cream takes 15 minutes of packaging. The pharmacy has 3000 minutes available for packaging. This gives us the constraint $10x_1 + 15x_2 \leq 3000$.

## 5: Non-negativity constraints
The number of bottles of pills and cream cannot be negative, so we have $x_1 \geq 0$ and $x_2 \geq 0$.

## 6: Symbolic representation in JSON format
```json
{
    'sym_variables': [('x1', 'bottles of pills'), ('x2', 'bottles of cream')],
    'objective_function': '50*x1 + 60*x2',
    'constraints': [
        '20*x1 + 15*x2 <= 4000',
        '10*x1 + 15*x2 <= 3000',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## 7: Convert the problem into Gurobi code in Python
```python
import gurobipy as gp

# Create a new model
m = gp.Model("Pharmacy_Optimization")

# Define the variables
x1 = m.addVar(name="bottles_of_pills", lb=0, vtype=gp.GRB.INTEGER)
x2 = m.addVar(name="bottles_of_cream", lb=0, vtype=gp.GRB.INTEGER)

# Define the objective function
m.setObjective(50*x1 + 60*x2, gp.GRB.MAXIMIZE)

# Add the constraints
m.addConstr(20*x1 + 15*x2 <= 4000, name="weighing_constraint")
m.addConstr(10*x1 + 15*x2 <= 3000, name="packaging_constraint")

# Solve the model
m.optimize()

# Print the solution
if m.status == gp.GRB.OPTIMAL:
    print(f"Optimal solution: {x1.varName} = {x1.x}, {x2.varName} = {x2.x}")
    print(f"Maximum profit: ${50*x1.x + 60*x2.x:.2f}")
else:
    print("The model is infeasible.")
```