## Step 1: Define the symbolic representation of the problem

Let's denote:
- $x_1$ as the number of surfboards
- $x_2$ as the number of skateboards

The objective is to maximize profit, where the profit per surfboard is $70 and per skateboard is $45. Therefore, the objective function can be represented as:
\[ \text{Maximize:} \quad 70x_1 + 45x_2 \]

## Step 2: Define the constraints based on the given resources

Each surfboard requires 5 units of wood and each skateboard requires 4 units of wood, with 700 units of wood available. This gives us the constraint:
\[ 5x_1 + 4x_2 \leq 700 \]

Each surfboard requires 3 units of paint and each skateboard requires 2 units of paint, with 320 units of paint available. This gives us the constraint:
\[ 3x_1 + 2x_2 \leq 320 \]

Additionally, $x_1 \geq 0$ and $x_2 \geq 0$ because the number of surfboards and skateboards cannot be negative.

## 3: Symbolic representation in JSON format

```json
{
    'sym_variables': [('x1', 'surfboards'), ('x2', 'skateboards')],
    'objective_function': '70*x1 + 45*x2',
    'constraints': [
        '5*x1 + 4*x2 <= 700',
        '3*x1 + 2*x2 <= 320',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## 4: Convert the problem into Gurobi code

```python
import gurobipy as gp

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

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

# Define the objective function
model.setObjective(70*x1 + 45*x2, gp.GRB.MAXIMIZE)

# Add constraints
model.addConstr(5*x1 + 4*x2 <= 700, name="wood_constraint")
model.addConstr(3*x1 + 2*x2 <= 320, name="paint_constraint")

# Solve the model
model.optimize()

# Print the solution
if model.status == gp.GRB.OPTIMAL:
    print(f"Optimal number of surfboards: {x1.varValue}")
    print(f"Optimal number of skateboards: {x2.varValue}")
    print(f"Maximum profit: ${model.objVal:.2f}")
else:
    print("The model is infeasible.")
```