## Problem Description and Symbolic Representation

The problem can be broken down into the following components:

- **Variables:**
  - $x_1$: Number of regular phones sold per day
  - $x_2$: Number of premium phones sold per day

- **Objective Function:**
  The objective is to maximize profit. Given that the profit per regular phone is $200 and per premium phone is $300, the objective function can be represented as:
  - Maximize $200x_1 + 300x_2$

- **Constraints:**
  - The daily demand for regular phones is at most 20: $x_1 \leq 20$
  - The daily demand for premium phones is at most 15: $x_2 \leq 15$
  - The company can only sell at most 30 phones total of either type per day: $x_1 + x_2 \leq 30$
  - Non-negativity constraints: $x_1 \geq 0, x_2 \geq 0$

## Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'regular phones'), ('x2', 'premium phones')],
    'objective_function': '200*x1 + 300*x2',
    'constraints': [
        'x1 <= 20',
        'x2 <= 15',
        'x1 + x2 <= 30',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## Gurobi Code in Python

```python
import gurobipy as gp

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

# Define variables
x1 = model.addVar(lb=0, ub=20, name="regular_phones")
x2 = model.addVar(lb=0, ub=15, name="premium_phones")

# Objective function: Maximize profit
model.setObjective(200*x1 + 300*x2, gp.GRB.MAXIMIZE)

# Constraints
model.addConstr(x1 <= 20, name="demand_regular")
model.addConstr(x2 <= 15, name="demand_premium")
model.addConstr(x1 + x2 <= 30, name="total_demand")

# Solve the model
model.optimize()

# Print solution
if model.status == gp.GRB.OPTIMAL:
    print("Optimal Solution:")
    print(f"Regular Phones: {x1.varValue}")
    print(f"Premium Phones: {x2.varValue}")
    print(f"Max Profit: {model.objVal}")
else:
    print("No optimal solution found.")
```