## 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 and the objective function, as well as the constraints.

- Let \(x_1\) be the number of bulldozers and \(x_2\) be the number of forklifts.

The objective is to maximize profit. The profit per bulldozer is $7000, and the profit per forklift is $6000. So, the objective function can be represented as:
\[ \text{Maximize:} \quad 7000x_1 + 6000x_2 \]

The constraints are based on the available time for assembly and QC:
- Each bulldozer takes 3 hours on the assembly line, and each forklift takes 2 hours. There are 600 hours available.
- Each bulldozer takes 2 hours of QC time, and each forklift takes 1.5 hours. There are 400 hours available.

So, the constraints can be represented as:
\[ 3x_1 + 2x_2 \leq 600 \]
\[ 2x_1 + 1.5x_2 \leq 400 \]
Also, \(x_1 \geq 0\) and \(x_2 \geq 0\), since the number of bulldozers and forklifts cannot be negative.

## Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'bulldozers'), ('x2', 'forklifts')],
    'objective_function': 'Maximize: 7000x1 + 6000x2',
    'constraints': [
        '3x1 + 2x2 <= 600',
        '2x1 + 1.5x2 <= 400',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## Gurobi Code in Python

```python
import gurobipy as gp

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

# Define the variables
x1 = model.addVar(name="bulldozers", lb=0, vtype=gp.GRB.INTEGER)
x2 = model.addVar(name="forklifts", lb=0, vtype=gp.GRB.INTEGER)

# Define the objective function
model.setObjective(7000*x1 + 6000*x2, gp.GRB.MAXIMIZE)

# Add the constraints
model.addConstr(3*x1 + 2*x2 <= 600, name="assembly_line_time")
model.addConstr(2*x1 + 1.5*x2 <= 400, name="qc_time")

# Solve the model
model.optimize()

# Print the solution
if model.status == gp.GRB.OPTIMAL:
    print(f"Optimal solution: {model.getVarByName('bulldozers').varValue} bulldozers, {model.getVarByName('forklifts').varValue} forklifts")
    print(f"Max Profit: ${model.objVal}")
else:
    print("No optimal solution found")
```