To solve this optimization problem, we first need to understand and translate the given natural language description into a symbolic representation. This involves defining variables for each type of unit (artillery batteries, armored companies, signal platoons), formulating the objective function based on these variables, and listing all constraints as semi-algebraic expressions.

Let's denote:
- \(x_0\) as the number of artillery batteries,
- \(x_1\) as the number of armored companies,
- \(x_2\) as the number of signal platoons.

The objective function is to minimize: \(7.17x_0 + 2.28x_1 + 2.97x_2\).

Constraints based on deployment weights and offensive capability ratings are:
1. Total deployment weight for all units must be at least 49 metric tons: \(2x_0 + 9x_1 + 13x_2 \geq 49\).
2. The total deployment weight of armored companies and signal platoons must be at least 33 metric tons: \(9x_1 + 13x_2 \geq 33\).
3. Minimum deployment weight for artillery batteries and armored companies is 53 metric tons: \(2x_0 + 9x_1 \geq 53\).
4. The total combined offensive capability rating from artillery batteries, armored companies, and signal platoons should be at least 52: \(7x_0 + 5x_1 + 6x_2 \geq 52\).
5. The total combined offensive capability rating from artillery batteries plus armored companies should be at least 43: \(7x_0 + 5x_1 \geq 43\).

Additionally, all variables must be integers (whole numbers) since we cannot have fractions of units.

Now, let's provide the symbolic representation as requested:

```json
{
    'sym_variables': [('x0', 'artillery batteries'), ('x1', 'armored companies'), ('x2', 'signal platoons')],
    'objective_function': '7.17*x0 + 2.28*x1 + 2.97*x2',
    'constraints': [
        '2*x0 + 9*x1 + 13*x2 >= 49',
        '9*x1 + 13*x2 >= 33',
        '2*x0 + 9*x1 >= 53',
        '7*x0 + 5*x1 + 6*x2 >= 52',
        '7*x0 + 5*x1 >= 43'
    ]
}
```

Given this formulation, we can now implement the solution using Gurobi in Python:

```python
from gurobipy import *

# Create a new model
m = Model("Optimization_Problem")

# Define variables
x0 = m.addVar(vtype=GRB.INTEGER, name="artillery_batteries")
x1 = m.addVar(vtype=GRB.INTEGER, name="armored_companies")
x2 = m.addVar(vtype=GRB.INTEGER, name="signal_platoons")

# Set objective function
m.setObjective(7.17*x0 + 2.28*x1 + 2.97*x2, GRB.MINIMIZE)

# Add constraints
m.addConstr(2*x0 + 9*x1 + 13*x2 >= 49, name="total_weight_constraint")
m.addConstr(9*x1 + 13*x2 >= 33, name="armored_signal_weight_constraint")
m.addConstr(2*x0 + 9*x1 >= 53, name="artillery_armored_weight_constraint")
m.addConstr(7*x0 + 5*x1 + 6*x2 >= 52, name="total_offensive_capability_constraint")
m.addConstr(7*x0 + 5*x1 >= 43, name="artillery_armored_offensive_constraint")

# Optimize model
m.optimize()

# Print results
for v in m.getVars():
    print(f"{v.varName}: {v.x}")

print("Objective:", m.objVal)
```