## 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 bookcases
- $x_2$ = number of coffee tables

The objective is to maximize profit. The profit on a bookcase is $90, and the profit on a coffee table is $65. So, the objective function can be written as:

Maximize: $90x_1 + 65x_2$

The constraints are:
- The wood shop can make a maximum of 40 bookcases: $x_1 \leq 40$
- The wood shop can make a maximum of 60 coffee tables: $x_2 \leq 60$
- It takes a worker 7 hours to make a bookcase and 5 hours to make a coffee table, with a maximum of 150 hours: $7x_1 + 5x_2 \leq 150$
- Non-negativity constraints: $x_1 \geq 0, x_2 \geq 0$

## Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'bookcases'), ('x2', 'coffee tables')],
    'objective_function': '90*x1 + 65*x2',
    'constraints': [
        'x1 <= 40',
        'x2 <= 60',
        '7*x1 + 5*x2 <= 150',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## Gurobi Code in Python

```python
import gurobipy as gp

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

# Define variables
x1 = model.addVar(lb=0, ub=40, name="bookcases")
x2 = model.addVar(lb=0, ub=60, name="coffee_tables")

# Objective function: maximize profit
model.setObjective(90*x1 + 65*x2, gp.GRB.MAXIMIZE)

# Constraints
model.addConstr(x1 <= 40, name="bookcase_limit")
model.addConstr(x2 <= 60, name="coffee_table_limit")
model.addConstr(7*x1 + 5*x2 <= 150, name="hour_limit")

# Solve the model
model.optimize()

# Print the solution
if model.status == gp.GRB.OPTIMAL:
    print("Optimal Solution:")
    print(f"Bookcases: {x1.varValue}")
    print(f"Coffee Tables: {x2.varValue}")
    print(f"Max Profit: {model.objVal}")
else:
    print("The model is infeasible.")
```