## Symbolic Representation

To solve the given problem, we first need to convert the natural language description into a symbolic representation.

Let's define the variables:
- $x_1$ = number of entry-level devices sold
- $x_2$ = number of premium devices sold

The objective is to maximize profit. The profit for each entry-level device is $300, and for each premium device is $200. So, the objective function can be represented as:

Maximize: $300x_1 + 200x_2$

The constraints based on the given information are:
1. Daily demand for entry-level devices is at most 20: $x_1 \leq 20$
2. Daily demand for premium devices is at most 15: $x_2 \leq 15$
3. The company can sell at most 30 devices total per day: $x_1 + x_2 \leq 30$
4. Non-negativity constraints: $x_1 \geq 0, x_2 \geq 0$

## Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'entry-level devices'), ('x2', 'premium devices')],
    'objective_function': '300*x1 + 200*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("Electronics_Company_Optimization")

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

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

# Constraints
model.addConstr(x1 <= 20, name="entry_level_demand")
model.addConstr(x2 <= 15, name="premium_demand")
model.addConstr(x1 + x2 <= 30, name="total_devices_sold")

# Solve the model
model.optimize()

# Print solution
if model.status == gp.GRB.OPTIMAL:
    print("Optimal Solution:")
    print(f"Entry-level devices to sell: {x1.varValue}")
    print(f"Premium devices to sell: {x2.varValue}")
    print(f"Max Profit: ${model.objVal:.2f}")
else:
    print("The model is infeasible.")
```